Optimal Bayesian Sampling Plans Based on Hybrid Ty

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非线性贝叶斯滤波算法综述_曲从善

得了很多有价值的研究成果。本文从递归贝叶斯估计的框架出发, 给出非线性滤波的统一描述, 并分门别类地对各种非线性滤波的原理、方法及特点做出分析和评述, 最后介绍了非线性滤波研究的新动态 , 并对其发展作了简单展望。
由上面的计算过程可以看出, 递归贝叶斯估计有两个步骤, 即式 ( 6) ( Chapman- Kolmogoro equation, CK 方程) 所示的贝叶斯预测步骤 ( 时间更新 ) 和式 ( 8) 所示的修正步骤 ( 量测更新 ) , 其过程如图 1 所 [ 17] 示。
| xk ) p ( x k | Yk- 1 ) d xk ( 7)
滤波和 Markov Chain Monte
等非线性滤波技术的研究 , 并取
3) 在 k 时刻 , 已经获得新的量测数据 y k , 可利用贝叶斯公式计算得到后验概率密度函数 p ( xk | Yk ) = p ( y k | xk ) p ( x k | Yk - 1 ) p ( y k | Yk - 1 ) ( 8)
x p( x Q
k ^ T
k
| Yk ) d xk
( 3)
Q
( x k - xk ) ( xk - x k ) p ( x k | Yk ) d xk ( 4)
^
式( 3) 可以推广到状态函数的估计而不是状态本身的估计 , 因此, 后验概率密度函数 p ( xk | Yk ) 在滤波理论中起着非常重要的作用。 p ( xk | Yk ) 封装了状态向量 x k 的所有信息 , 因为它同时蕴含了量测 Yk 和先验分布 x k - 1 的信息。在给定先验密度 p ( x k - 1 | Yk - 1 ) 以及最近的观测 y k 时 , 通过式 ( 5) 所示的贝叶斯定理来计算后验概率密度

基于模态应变能的不同损伤指标对比

基于模态应变能的不同损伤指标对比郭惠勇;盛懋【摘要】为解决工程结构的多损伤识别问题，对基于模态应变能的不同损伤指标方法进行了对比分析和研究。

首先，描述了3种损伤指标，即模态应变能变化指标( MSECI)、模态应变能耗散率指标( MSECRI)和模态应变能基指标( MSEBI)；然后借鉴模态应变能耗散率指标的建立原理，通过对刚度矩阵的修正，建立相应的能量等效方程，并提取了一种模态应变能等效指标( MSEEI )；最后对4种应变能损伤指标进行了对比研究，并考虑了测量噪声的影响。

数值仿真结果表明，模态应变能基指标可以较好地识别结构的损伤位置，模态应变能等效指标则不仅可以有效地识别结构的损伤位置，而且可以较为精确地识别结构的损伤程度。

%In order to solve the problem of structural multi-damage identification, different modal strain energy damage index methods are studied and compared. First, three kinds of modal strain energy damage indices, the modal strain energy change index, the modal strain energy dissipation ratio index, and the modal strain energy based index, are described. Then, with consideration of the modal strain energy dissipation ratio index method, an improved equivalence equation is derived through improvement of the stiffness matrix, and a modal strain energy equivalence index is proposed. Finally, four kinds of modal strain energy damage indices are compared, and the impact of the measured noise is considered. Simulation results show that the modal strain energy-based index method can identify structural damage locations, and the proposed modal strain energyequivalence index can not only identify structural damage locations but can identify the damage extent with high accuracy.【期刊名称】《河海大学学报（自然科学版）》【年(卷),期】2014(000)005【总页数】7页(P444-450)【关键词】损伤识别;模态应变能;应变能变化率;应变能耗散率;应变能基指标【作者】郭惠勇;盛懋【作者单位】重庆大学土木工程学院，重庆 400045; 重庆大学山地城镇建设与新技术教育部重点实验室，重庆 400045;重庆大学土木工程学院，重庆 400045; 重庆大学山地城镇建设与新技术教育部重点实验室，重庆 400045【正文语种】中文【中图分类】TU312+.3工程结构的损伤识别研究是国际上的研究热点[1-5]。

AI术语

人工智能专业重要词汇表1、A开头的词汇：Artificial General Intelligence/AGI通用人工智能Artificial Intelligence/AI人工智能Association analysis关联分析Attention mechanism注意力机制Attribute conditional independence assumption属性条件独立性假设Attribute space属性空间Attribute value属性值Autoencoder自编码器Automatic speech recognition自动语音识别Automatic summarization自动摘要Average gradient平均梯度Average-Pooling平均池化Accumulated error backpropagation累积误差逆传播Activation Function激活函数Adaptive Resonance Theory/ART自适应谐振理论Addictive model加性学习Adversarial Networks对抗网络Affine Layer仿射层Affinity matrix亲和矩阵Agent代理/ 智能体Algorithm算法Alpha-beta pruningα-β剪枝Anomaly detection异常检测Approximation近似Area Under ROC Curve／AUC R oc 曲线下面积2、B开头的词汇Backpropagation Through Time通过时间的反向传播Backpropagation/BP反向传播Base learner基学习器Base learning algorithm基学习算法Batch Normalization/BN批量归一化Bayes decision rule贝叶斯判定准则Bayes Model Averaging／BMA贝叶斯模型平均Bayes optimal classifier贝叶斯最优分类器Bayesian decision theory贝叶斯决策论Bayesian network贝叶斯网络Between-class scatter matrix类间散度矩阵Bias偏置/ 偏差Bias-variance decomposition偏差-方差分解Bias-Variance Dilemma偏差–方差困境Bi-directional Long-Short Term Memory/Bi-LSTM双向长短期记忆Binary classification二分类Binomial test二项检验Bi-partition二分法Boltzmann machine玻尔兹曼机Bootstrap sampling自助采样法／可重复采样／有放回采样Bootstrapping自助法Break-Event Point／BEP平衡点3、C开头的词汇Calibration校准Cascade-Correlation级联相关Categorical attribute离散属性Class-conditional probability类条件概率Classification and regression tree/CART分类与回归树Classifier分类器Class-imbalance类别不平衡Closed -form闭式Cluster簇/类/集群Cluster analysis聚类分析Clustering聚类Clustering ensemble聚类集成Co-adapting共适应Coding matrix编码矩阵COLT国际学习理论会议Committee-based learning基于委员会的学习Competitive learning竞争型学习Component learner组件学习器Comprehensibility可解释性Computation Cost计算成本Computational Linguistics计算语言学Computer vision计算机视觉Concept drift概念漂移Concept Learning System /CLS概念学习系统Conditional entropy条件熵Conditional mutual information条件互信息Conditional Probability Table／CPT条件概率表Conditional random field/CRF条件随机场Conditional risk条件风险Confidence置信度Confusion matrix混淆矩阵Connection weight连接权Connectionism连结主义Consistency一致性／相合性Contingency table列联表Continuous attribute连续属性Convergence收敛Conversational agent会话智能体Convex quadratic programming凸二次规划Convexity凸性Convolutional neural network/CNN卷积神经网络Co-occurrence同现Correlation coefficient相关系数Cosine similarity余弦相似度Cost curve成本曲线Cost Function成本函数Cost matrix成本矩阵Cost-sensitive成本敏感Cross entropy交叉熵Cross validation交叉验证Crowdsourcing众包Curse of dimensionality维数灾难Cut point截断点Cutting plane algorithm割平面法4、D开头的词汇Data mining数据挖掘Data set数据集Decision Boundary决策边界Decision stump决策树桩Decision tree决策树／判定树Deduction演绎Deep Belief Network深度信念网络Deep Convolutional Generative Adversarial Network/DCGAN深度卷积生成对抗网络Deep learning深度学习Deep neural network/DNN深度神经网络Deep Q-Learning深度Q 学习Deep Q-Network深度Q 网络Density estimation密度估计Density-based clustering密度聚类Differentiable neural computer可微分神经计算机Dimensionality reduction algorithm降维算法Directed edge有向边Disagreement measure不合度量Discriminative model判别模型Discriminator判别器Distance measure距离度量Distance metric learning距离度量学习Distribution分布Divergence散度Diversity measure多样性度量／差异性度量Domain adaption领域自适应Downsampling下采样D-separation （Directed separation）有向分离Dual problem对偶问题Dummy node哑结点Dynamic Fusion动态融合Dynamic programming动态规划5、E开头的词汇Eigenvalue decomposition特征值分解Embedding嵌入Emotional analysis情绪分析Empirical conditional entropy经验条件熵Empirical entropy经验熵Empirical error经验误差Empirical risk经验风险End-to-End端到端Energy-based model基于能量的模型Ensemble learning集成学习Ensemble pruning集成修剪Error Correcting Output Codes／ECOC纠错输出码Error rate错误率Error-ambiguity decomposition误差-分歧分解Euclidean distance欧氏距离Evolutionary computation演化计算Expectation-Maximization期望最大化Expected loss期望损失Exploding Gradient Problem梯度爆炸问题Exponential loss function指数损失函数Extreme Learning Machine/ELM超限学习机6、F开头的词汇Factorization因子分解False negative假负类False positive假正类False Positive Rate/FPR假正例率Feature engineering特征工程Feature selection特征选择Feature vector特征向量Featured Learning特征学习Feedforward Neural Networks/FNN前馈神经网络Fine-tuning微调Flipping output翻转法Fluctuation震荡Forward stagewise algorithm前向分步算法Frequentist频率主义学派Full-rank matrix满秩矩阵Functional neuron功能神经元7、G开头的词汇Gain ratio增益率Game theory博弈论Gaussian kernel function高斯核函数Gaussian Mixture Model高斯混合模型General Problem Solving通用问题求解Generalization泛化Generalization error泛化误差Generalization error bound泛化误差上界Generalized Lagrange function广义拉格朗日函数Generalized linear model广义线性模型Generalized Rayleigh quotient广义瑞利商Generative Adversarial Networks/GAN生成对抗网络Generative Model生成模型Generator生成器Genetic Algorithm/GA遗传算法Gibbs sampling吉布斯采样Gini index基尼指数Global minimum全局最小Global Optimization全局优化Gradient boosting梯度提升Gradient Descent梯度下降Graph theory图论Ground-truth真相／真实8、H开头的词汇Hard margin硬间隔Hard voting硬投票Harmonic mean调和平均Hesse matrix海塞矩阵Hidden dynamic model隐动态模型Hidden layer隐藏层Hidden Markov Model/HMM隐马尔可夫模型Hierarchical clustering层次聚类Hilbert space希尔伯特空间Hinge loss function合页损失函数Hold-out留出法Homogeneous同质Hybrid computing混合计算Hyperparameter超参数Hypothesis假设Hypothesis test假设验证9、I开头的词汇ICML国际机器学习会议Improved iterative scaling/IIS改进的迭代尺度法Incremental learning增量学习Independent and identically distributed/i.i.d.独立同分布Independent Component Analysis/ICA独立成分分析Indicator function指示函数Individual learner个体学习器Induction归纳Inductive bias归纳偏好Inductive learning归纳学习Inductive Logic Programming／ILP归纳逻辑程序设计Information entropy信息熵Information gain信息增益Input layer输入层Insensitive loss不敏感损失Inter-cluster similarity簇间相似度International Conference for Machine Learning/ICML国际机器学习大会Intra-cluster similarity簇内相似度Intrinsic value固有值Isometric Mapping/Isomap等度量映射Isotonic regression等分回归Iterative Dichotomiser迭代二分器10、K开头的词汇Kernel method核方法Kernel trick核技巧Kernelized Linear Discriminant Analysis／KLDA核线性判别分析K-fold cross validation k 折交叉验证／k 倍交叉验证K-Means Clustering K –均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base知识库Knowledge Representation知识表征11、L开头的词汇Label space标记空间Lagrange duality拉格朗日对偶性Lagrange multiplier拉格朗日乘子Laplace smoothing拉普拉斯平滑Laplacian correction拉普拉斯修正Latent Dirichlet Allocation隐狄利克雷分布Latent semantic analysis潜在语义分析Latent variable隐变量Lazy learning懒惰学习Learner学习器Learning by analogy类比学习Learning rate学习率Learning Vector Quantization/LVQ学习向量量化Least squares regression tree最小二乘回归树Leave-One-Out/LOO留一法linear chain conditional random field线性链条件随机场Linear Discriminant Analysis／LDA线性判别分析Linear model线性模型Linear Regression线性回归Link function联系函数Local Markov property局部马尔可夫性Local minimum局部最小Log likelihood对数似然Log odds／logit对数几率Logistic Regression Logistic 回归Log-likelihood对数似然Log-linear regression对数线性回归Long-Short Term Memory/LSTM长短期记忆Loss function损失函数12、M开头的词汇Machine translation/MT机器翻译Macron-P宏查准率Macron-R宏查全率Majority voting绝对多数投票法Manifold assumption流形假设Manifold learning流形学习Margin theory间隔理论Marginal distribution边际分布Marginal independence边际独立性Marginalization边际化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field马尔可夫随机场Maximal clique最大团Maximum Likelihood Estimation/MLE极大似然估计／极大似然法Maximum margin最大间隔Maximum weighted spanning tree最大带权生成树Max-Pooling最大池化Mean squared error均方误差Meta-learner元学习器Metric learning度量学习Micro-P微查准率Micro-R微查全率Minimal Description Length/MDL最小描述长度Minimax game极小极大博弈Misclassification cost误分类成本Mixture of experts混合专家Momentum动量Moral graph道德图／端正图Multi-class classification多分类Multi-document summarization多文档摘要Multi-layer feedforward neural networks多层前馈神经网络Multilayer Perceptron/MLP多层感知器Multimodal learning多模态学习Multiple Dimensional Scaling多维缩放Multiple linear regression多元线性回归Multi-response Linear Regression ／MLR多响应线性回归Mutual information互信息13、N开头的词汇Naive bayes朴素贝叶斯Naive Bayes Classifier朴素贝叶斯分类器Named entity recognition命名实体识别Nash equilibrium纳什均衡Natural language generation/NLG自然语言生成Natural language processing自然语言处理Negative class负类Negative correlation负相关法Negative Log Likelihood负对数似然Neighbourhood Component Analysis/NCA近邻成分分析Neural Machine Translation神经机器翻译Neural Turing Machine神经图灵机Newton method牛顿法NIPS国际神经信息处理系统会议No Free Lunch Theorem／NFL没有免费的午餐定理Noise-contrastive estimation噪音对比估计Nominal attribute列名属性Non-convex optimization非凸优化Nonlinear model非线性模型Non-metric distance非度量距离Non-negative matrix factorization非负矩阵分解Non-ordinal attribute无序属性Non-Saturating Game非饱和博弈Norm范数Normalization归一化Nuclear norm核范数Numerical attribute数值属性14、O开头的词汇Objective function目标函数Oblique decision tree斜决策树Occam’s razor奥卡姆剃刀Odds几率Off-Policy离策略One shot learning一次性学习One-Dependent Estimator／ODE独依赖估计On-Policy在策略Ordinal attribute有序属性Out-of-bag estimate包外估计Output layer输出层Output smearing输出调制法Overfitting过拟合／过配Oversampling过采样15、P开头的词汇Paired t-test成对t 检验Pairwise成对型Pairwise Markov property成对马尔可夫性Parameter参数Parameter estimation参数估计Parameter tuning调参Parse tree解析树Particle Swarm Optimization/PSO粒子群优化算法Part-of-speech tagging词性标注Perceptron感知机Performance measure性能度量Plug and Play Generative Network即插即用生成网络Plurality voting相对多数投票法Polarity detection极性检测Polynomial kernel function多项式核函数Pooling池化Positive class正类Positive definite matrix正定矩阵Post-hoc test后续检验Post-pruning后剪枝potential function势函数Precision查准率／准确率Prepruning预剪枝Principal component analysis/PCA主成分分析Principle of multiple explanations多释原则Prior先验Probability Graphical Model概率图模型Proximal Gradient Descent/PGD近端梯度下降Pruning剪枝Pseudo-label伪标记16、Q开头的词汇Quantized Neural Network量子化神经网络Quantum computer量子计算机Quantum Computing量子计算Quasi Newton method拟牛顿法17、R开头的词汇Radial Basis Function／RBF径向基函数Random Forest Algorithm随机森林算法Random walk随机漫步Recall查全率／召回率Receiver Operating Characteristic/ROC受试者工作特征Rectified Linear Unit/ReLU线性修正单元Recurrent Neural Network循环神经网络Recursive neural network递归神经网络Reference model参考模型Regression回归Regularization正则化Reinforcement learning/RL强化学习Representation learning表征学习Representer theorem表示定理reproducing kernel Hilbert space/RKHS再生核希尔伯特空间Re-sampling重采样法Rescaling再缩放Residual Mapping残差映射Residual Network残差网络Restricted Boltzmann Machine/RBM受限玻尔兹曼机Restricted Isometry Property/RIP限定等距性Re-weighting重赋权法Robustness稳健性/鲁棒性Root node根结点Rule Engine规则引擎Rule learning规则学习18、S开头的词汇Saddle point鞍点Sample space样本空间Sampling采样Score function评分函数Self-Driving自动驾驶Self-Organizing Map／SOM自组织映射Semi-naive Bayes classifiers半朴素贝叶斯分类器Semi-Supervised Learning半监督学习semi-Supervised Support Vector Machine半监督支持向量机Sentiment analysis情感分析Separating hyperplane分离超平面Sigmoid function Sigmoid 函数Similarity measure相似度度量Simulated annealing模拟退火Simultaneous localization and mapping同步定位与地图构建Singular Value Decomposition奇异值分解Slack variables松弛变量Smoothing平滑Soft margin软间隔Soft margin maximization软间隔最大化Soft voting软投票Sparse representation稀疏表征Sparsity稀疏性Specialization特化Spectral Clustering谱聚类Speech Recognition语音识别Splitting variable切分变量Squashing function挤压函数Stability-plasticity dilemma可塑性-稳定性困境Statistical learning统计学习Status feature function状态特征函Stochastic gradient descent随机梯度下降Stratified sampling分层采样Structural risk结构风险Structural risk minimization/SRM结构风险最小化Subspace子空间Supervised learning监督学习／有导师学习support vector expansion支持向量展式Support Vector Machine/SVM支持向量机Surrogat loss替代损失Surrogate function替代函数Symbolic learning符号学习Symbolism符号主义Synset同义词集19、T开头的词汇T-Distribution Stochastic Neighbour Embedding/t-SNE T–分布随机近邻嵌入Tensor张量Tensor Processing Units/TPU张量处理单元The least square method最小二乘法Threshold阈值Threshold logic unit阈值逻辑单元Threshold-moving阈值移动Time Step时间步骤Tokenization标记化Training error训练误差Training instance训练示例／训练例Transductive learning直推学习Transfer learning迁移学习Treebank树库Tria-by-error试错法True negative真负类True positive真正类True Positive Rate/TPR真正例率Turing Machine图灵机Twice-learning二次学习20、U开头的词汇Underfitting欠拟合／欠配Undersampling欠采样Understandability可理解性Unequal cost非均等代价Unit-step function单位阶跃函数Univariate decision tree单变量决策树Unsupervised learning无监督学习／无导师学习Unsupervised layer-wise training无监督逐层训练Upsampling上采样21、V开头的词汇Vanishing Gradient Problem梯度消失问题Variational inference变分推断VC Theory VC维理论Version space版本空间Viterbi algorithm维特比算法Von Neumann architecture冯·诺伊曼架构22、W开头的词汇Wasserstein GAN/WGAN Wasserstein生成对抗网络Weak learner弱学习器Weight权重Weight sharing权共享Weighted voting加权投票法Within-class scatter matrix类内散度矩阵Word embedding词嵌入Word sense disambiguation词义消歧23、Z开头的词汇Zero-data learning零数据学习Zero-shot learning零次学习。

地统计与遥感---专业英语词汇

地统计以及遥感英文词汇300个：gray level co-occurrence matrix algorithm灰度共生矩阵算法characteristic of atmospheric transmission 大气传输特性earth resources technology satellite,ERTS 地球资源卫星Land-use and land-over change 土地利用土地覆盖变化Multi-stage stratified random sample 多级分层随机采样Normalized Difference Vegetation Index归一化植被指数Soil-Adjusted Vegetation Index土壤调整植被指数Modified Soil-Adjusted Vegetation Index修正土壤调整植被指数image resolution ,ground resolution影象分辨力(又称“象元地面分辨力”。

指象元地面尺寸。

) remote sensing information transmission遥感信息传输remote sensing information acquisition遥感信息获取multi- spectral remote sensing technology多光谱遥感技术Availability and accessibility 可用性和可获取性Association of Geographic Information (AGI) 地理信息协会Difference Vegetation Index差值植被指数image quality 影象质量Enhanced Vegetation Index增强型植被指数Ratio Vegetation Index比值植被指数Spatial autocorrelation 空间自相关Lag Size 滞后尺寸Ordinary kriging 普通克里金Indicator kriging 指示克里金Disjunctive kriging 析取克里金Simple kriging 简单克里金Bivariate normal distributions 双变量正态分布Universal kriging 通用克里金conditional simulation 条件模拟image filtering 图像滤波optimal sampling strategy 最优采样策略temporal and spatial patterns 时空格局Instantaneous field-of-view瞬时视场角azimuth 方位角wavelet transform method 小波变换算法priori probability 先验概率geometric distortion 几何畸变active remote sensing主动式遥感passive remote sensing 被动式遥感multispectral remote sensing多谱段遥感multitemporal remote sensing 多时相遥感infrared remote sensing 红外遥感microwave remote sensing微波遥感quantizing，quantization量化sampling interval 采样间隔digital mapping数字测图digital elevation model,DEM 数字高程模型digital surface model,DSM 数字表面模型solar radiation spectrum太阳辐射波谱atmospheric window 大气窗atmospheric transmissivity大气透过率atmospheric noise 大气噪声atmospheric refraction 大气折射atmospheric attenuation 大气衰减back scattering 后向散射annotation 注解spectrum character curve 波谱特征曲线spectrum response curve 波谱响应曲线spectrum feature space波谱特征空间spectrum cluster 波谱集群infrared spectrum 红外波谱reflectance spectrum反射波谱electro-magnetic spectrum 电磁波谱object spectrum characteristic地物波谱特性thermal radiation 热辐射microwave radiation微波辐射data acquisition数据获取data transmission数据传输data processing 数据处理ground receiving station地面接收站environmental survey satellite环境探测卫星geo-synchronous satellite地球同步卫星sun-synchronous satellite太阳同步卫星satellite attitude卫星姿态remote sensing platform 遥感平台static sensor 静态传感器dynamic sensor动态传感器optical sensor光学传感器microwave remote sensor微波传感器photoelectric sensor光电传感器radiation sensor辐射传感器satellite-borne sensor星载传感器airborne sensor机载传感器attitude-measuring sensor 姿态测量传感器image mosai图象镶嵌c image digitisation图象数字化ratio transformation比值变换biomass index transformation生物量指标变换tesseled cap transformation 穗帽变换reference data 参照数据image enhancement 图象增强edge enhanceme边缘增强ntedge detection边缘检测contrast enhancement反差增强texture enhancement 纹理增强ratio enhancement 比例增强texture analysis 纹理分析color enhancement 彩色增强pattern recognition 模式识别classifier 分类器supervised classification监督分类unsupervised classification非监督分类box classifier method 盒式分类法fuzzy classifier method 模糊分类法maximum likelihood classification最大似然分类minimum distance classification最小距离分类Bayesian classification 贝叶斯分类Computer-assisted classification机助分类illumination 照度principal component analysis 主成分分析spectral mixture analysis 混合像元分解fuzzy sets 模糊数据集topographic correction 地形校正ground truth data 地面真实数据Tasselled cap 缨帽变换Artificial neural networks 人工神经网络Visual interpretation 目视解译accuracy assessment 精度评价Omission error漏分误差commission error 错分误差Multi-source data 多源数据heterogeneous 非均质的Training sample 训练样本ancillary data 辅助数据dark-object subtraction 暗目标相减法discriminant analysis 判别分析‘salt and pepper’ effects 椒盐效应spectral confusion光谱混淆Cluster sampling 聚簇采样systematic sampling 系统采样Error matrix误差矩阵hard classification 硬分类Soft classification 软分类decision tree classifier 决策树分类器Spectral angle classifier 光谱角分类器support vector machine支持向量机Fuzzy expert system 模糊专家系统endmember spectral端元光谱Future extraction 特征提取image mosaic图像镶嵌density slicing密度分割least squares correlation 最小二乘相关data fusion 数据融合Image segmentation图像分割urban remote sensing 城市遥感atmospheric remote sensing大气遥感geomorphological remote sensing地貌遥感ground resolution地面分辨率ground date processing system地面数据处理系统ground remote sensing地面遥感object spectrum characteristic地物波谱特性space characteristic of object地物空间特性geological remote sensing地质遥感multispectral remote sensing多光谱遥感optical remote sensing technology光学遥感技术ocean remote sensing海洋遥感marine resource remote sensing海洋资源遥感aerial remote sensing航空遥感space photography航天摄影space remote sensing航天遥感infrared remote sensing红外遥感infrared remote sensing technology红外遥感技术environmental remote sensing环境遥感laser remote sensing激光遥感polar region remote sensing极地遥感visible light remote sensing可见光遥感range resolution空间分辨率radar remote sensing雷达遥感forestry remote sensing林业遥感agricultural remote sensing农业遥感forest remote sensing森林遥感water resources remote sensing水资源遥感land resource remote sensing土地资源遥感microwave emission微波辐射microwave remote sensing微波遥感microwave remote sensing technology微波遥感技术remote sensing sounding system遥感测深系统remote sensing estimation遥感估产remote sensing platform遥感平台satellite of remote sensing遥感卫星remote sensing instrument遥感仪器remote sensing image遥感影像remote sensing cartography遥感制图remote sensing expert system遥感专家系统active remote sensing主动式遥感passive remote sensing被动式遥感resource remote sensing资源遥感ultraviolet remote sensing紫外遥感attributive geographic data 属性地理数据attributes, types 属性，类型Geographic database types 地理数据库类型attribute data 属性数据Geographic individual 地理个体Geographic information (GI) 地理信息Exponential transform指数变换false colour composite 假彩色合成Image recognition 图像识别image scale 图像比例尺Spatial frequency 空间频率spectral resolution 光谱分辨率Logarithmic transform对数变换mechanism of remote sensing 遥感机理adret 阳坡beam width波束宽度biosphere生物圈curve fitting 曲线拟合geostationary satellite对地静止卫星glacis缓坡Field check 野外检查grating 光栅gray scale 灰阶Interactive 交互式interference干涉inversion 反演Irradiance 辐照度landsatscape 景观isoline 等值线Lidar激光雷达landform analysis地形分析legend 图例Map projection地图投影map revision地图更新Middle infrared中红外Mie scattering 米氏散射opaco 阴坡orbital period 轨道周期Overlap重叠parallax 视差polarization 极化Phase 相位pattern 图案quadtree象限四分树Radar returns雷达回波rayleigh scattering 瑞利散射reflectance 反射率Ridge山脊saturation 饱和度solar elevation太阳高度角Subset 子集telemetry遥测surface roughness表面粗糙度Thematic map专题制图thermal infrared热红外uniformity均匀性Upland 高地vegetal cover 植被覆盖watershed流域White plate白板zenith angle天顶角radiant flux 辐射通量Aerosol 气溶胶all weather 全天候angle of field 视场角Aspect 坡向atmospheric widow大气窗口atmospheric 大气圈Path radiance 路径辐射binary code二进制码black body 黑体Cloud cover云覆盖confluence 汇流点diffuse reflection漫反射Distortion畸变divide分水岭entropy熵meteosat气象卫星bulk processing粗处理precision processing精处理Bad lines 坏带single-date image单时相影像Decompose 分解threshold 阈值relative calibration 相对校正post-classification 分类后处理Aerophotograph 航片Base map 底图muti-temporal datasets 多时相数据集detector 探测器spectrograph 摄谱仪spectrometer 波谱测定仪Geostatistics 地统计Semivariogram 半方差sill 基台Nugget 块金Range 变程Kriging 克里金CoKriging 共协克里金Anisotropic 各向异性Isotropic 各向同性scale 尺度regional variable 区域变量transect 横断面Interpolation 插值heterogeneity 异质性texture 纹理digital rectification数字纠正digital mosaic 数字镶嵌image matching影像匹配density 密度grey level灰度pixel,picture element 象元target area目标区searching area 搜索区Spacelab 空间实验室space shuttle航天飞机Landsat陆地卫星Seasat 海洋卫星Mapsat测图卫星Stereosat 立体卫星aspatial data 非空间数据。

sampling-based strategies 模型推理

sampling-based strategies 模型推理
Sampling-based strategies 是一种模型推理方法，它通过从模型的可能输出中采样来近似计算模型的输出。

这种方法通常用于处理概率模型，例如贝叶斯网络或隐马尔可夫模型，其中模型的输出是概率分布。

在采样过程中，通常会使用一些重复抽样的技术来获取足够的样本以获得可靠的结果。

这种方法适用于各种任务，例如概率建模、蒙特卡洛模拟、机器学习等。

使用 Sampling-based strategies 的优点是可以处理复杂的概率模型，并且可以获得更准确的估计结果。

然而，这种方法也有一些缺点，例如计算量大、需要大量的样本才能获得可靠的结果等。

总之，Sampling-based strategies 是一种非常有用的模型推理方法，它可以用于处理各种概率模型和任务。

博弈论贝叶斯博弈与贝叶斯均衡ppt课件

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Bayesian Nash Equilibrium
Department of Mathematics
不完全信息博弈问题
将博弈开始时就存在事前不确定性的博弈问题称为不完全信息博弈问题。
Department of Mathematics
例子：斗鸡博弈
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Department of Mathematics
Example: Scalping Tickets
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Department of Mathematics

“机器学习”课程教学大纲.

“机器学习”课程教学大纲一、课程名称：机器学习二、学分：3三、先修课程：高等数学、计算方法、概率论四、开课目的本课程是面向数学科学学院、信息科学学院研究生开设的专业基础课（高年级本科生可选修）。

其教学目的是使学生掌握常见“机器学习”类型：监督学习、无监督学习、半监督学习和强化学习中的主要学习算法，包括算法的主要思想和基本步骤，并通过编程练习和典型应用实例加深了解；同时对机器学习的一般理论，如计算学习理论、采样理论等有所了解。

要求选课学生事先受过基本编程训练，熟悉C/C++或Matlab编程语言，具有多元微积分、高等代数和概率统计方面基本知识。

五、教材主要参考书：“Machine Learning”by Tom Mitchell，辅助参考书：“Pattern Recognition and Machine Learning”by Christopher Bishop；“Pattern Recognition”by Richard Duda et al。

六、课程进度课程内容主要由机器学习简介、监督学习、计算学习理论、无监督学习、半监督学习、增强学习组成，其中监督学习包括决策树、贝叶斯学习、核方法、神经网络、图模型，和隐马尔科夫模型，以下是课程进度，括号中的数字为大约所需授课时间。

1)Introduction to machine learning (2hr)2)Inductive learning, decision tree (2hr)3)Evaluating hypotheses, covering Estimating hypothesis accuracy, Basics of sampling theory,and Comparing learning algorithm (3hr).4)Bayesian learning, covering Bayesian theory, Maximum likelihood and MAP estimators,Minimum Description Length Principle, Bayes Optimal Classifier, and Naïve Bayesianclassifier (3hr).5)Computational learning theory, covering PAC learning, VC dimension etc. (3hr)6)Kernel methods, covering Dual representations, Constructing kernels, Radical basisfunction networks, and Gaussian process (4hr).7)Artificial neural network, covering perceptron, multilayer network and the backpropagationalgorithm, and facial recognition as an example (3hr).8)Graphical models, covering Bayesian network, Conditional independence, Markov RandomFields, and Inference in graphical models (8hr)9)Sampling methods, covering Basic sampling algorithms and Markov Chain Monte Carlo(3hr).10)Markov model and Hidden Markov model (HMM), and application of HMM in speechrecognition (3hr).11)Clustering, focusing on Density-based clustering and Hierarchical clustering (2hr).12)Semi-supervised learning (3hr)13)Reinforcement learning, covering Q learning etc. (3hr)在上述内容中1–5、7和13主要出自Mitchell著作中的相关章节，6和8–10主要出自Bishop 著作中的相关章节。

A sampling-based algorithm for multi-robot visibility-based pursuit-evasion

A Sampling-Based Algorithm for Multi-Robot Visibility-BasedPursuit-EvasionNicholas M.Stifﬂer Jason M.O’KaneAbstract—We introduce a probabilistically complete algo-rithm for solving a visibility-based pursuit-evasion problem in two-dimensional polygonal environments with multiple pur-suers.The inputs for our algorithm are an environment and the initial positions of the pursuers.The output is a joint strategy for the pursuers that guarantees that the evader has been captured.We create a Sample-Generated Pursuit-Evasion Graph(SG-PEG)that utilizes an abstract sample generator to search the pursuers’joint conﬁguration space for a pursuer solution strategy that captures the evaders.We implemented our algorithm in simulation and provide results.I.I NTRODUCTIONThere are many variants of the pursuit-evasion problem. The common theme amongst them is that one group of agents,the“pursuers”,attempts to track members of another group,the“evaders”.This paper considers a speciﬁc variant of the pursuit-evasion problem called visibility-based pursuit-evasion, which requires the pursuer(s)to systematically search an environment to locate the evaders,ensuring that all evaders will be found by the pursuers in aﬁnite time.The speciﬁc problem we consider is a visibility-based pursuit-evasion problem that utilizes a team of pursuers.The pursuers move through a polygonal environment seeking to locate an unknown number of evaders,which move at aﬁnite but unbounded speed.The pursuers have an omni-directional ﬁeld-of-view that extends to the environment boundary.The goal is toﬁnd a joint strategy for the pursuers that ensures that all of the evaders are seen.The visibility-based pursuit-evasion problem has an extra layer of complexity beyond the standard motion planning problem because of its capture guarantee.It is not enough to simply select a standard motion planner and attempt to generate a path for each pursuer through the environment. To guarantee that the pursuer strategy does indeed capture an evader if one exists,the planner must also reason about the regions of the environment that are not currently in the pursuers’visualﬁeld-of-view and how these regions interact with one another as the pursuers move within the environment.Two dominant threads of research involve the number of deployable pursuers available to solve the visibility-based pursuit-evasion ing only a single pursuer, there are results that yield complete[4],randomized[8], and optimal[22]solutions,as well as many other variants N.M.Stifﬂer and J.M.O’Kane are with the Department of Computer Science and Engineering,University of South Carolina,301Main St., Columbia,SC29208,USA.{stifﬂen,jokane}@ Fig.1:A pursuer strategy generated by our algorithm.Filled circles represent the pursuers’initial positions and open circles represent their goal positions.discussed in Section II.A consequence of using only a single pursuer is that these algorithms are only applicable when the environment can be represented as a simply-connected polygon.The authors considered the multiple pursuer visibility-based pursuit-evasion problem[23]in the past.In that work,we introduced a centralized algorithm for computing a pursuer solution strategy.The general idea is to create a Cylindrical Algebraic Decomposition(CAD)of the pursuers’joint conﬁguration space by using polynomials that capture where critical changes to the regions of the environment hidden from the pursuers occur.Then we compute the adjacency graph for the CAD and construct a Pursuit Evasion Graph(PEG)induced by the adjacency graph.A search through the PEG can produce one of the following outcomes: the search can reach a vertex where the pursuers’motions up to this point ensure that the evader has been captured,or the search terminates withoutﬁnding a solution and produces a statement recognizing that no solution exists.The drawback of the technique is the computational complexity required to construct the CAD and perform the adjacency test,which is doubly exponential in the number of pursuers.This paper differs from that work in that we no longer discretize the conﬁguration space and maintain a CAD nor compute the adjacency graph.The main contribution of this work is a probabilisti-cally complete algorithm for multiple pursuer visibility-based pursuit-evasion that generates a solution strategy for the2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) September 14-18, 2014, Chicago, IL, USApursuers to execute(Figure1)through the joint conﬁguration space.Our algorithm creates a graph that maintains the pur-suers’information state,and utilizes a sample generator that we treat as a“black box”to reason about unexplored areas in the pursuers’joint conﬁguration space.Our algorithm has some similarity to the Probabilistic Roadmap(PRM) algorithm[10],but differs in that our algorithm maintains information concerning the areas of the environment where the evader might be.The need for this additional information complicates both the update operations for the graph and the selection of samples.The remainder of this paper is structured as follows.In Section II we discuss related work to our problem.Section III contains a formal problem statement.A formal deﬁnition for the area not visible to the pursuers,called shadows,appears in Section IV.This paper makes several new contributions: 1)We introduce a graph that maintains a representation ofthe reachable parts of the pursuers’joint information space and provide details about its construction(Sec-tion V).2)We introduce an algorithm that uses this graph to searchfor a pursuer solution strategy(Section VI).3)We present simulation results(Section VII)that showour algorithm’s ability to generate solution strategies for various sample generators.Discussion and concluding remarks appear in Section VIII.II.R ELATED W ORKThe pursuit-evasion problem was originally posed in the context of differential games[5],[7].The lion and man game and the homicidal chauffeur are two such differential games. In the lion and man game,a lion tries to capture a man who is trying to escape[15],[21].In game theory,the homicidal chauffeur is a pursuit-evasion problem which pits a slowly moving but highly maneuverable runner against the driver of a vehicle,which is faster but less maneuverable,who is attempting to run him over[7],[19].Theﬁrst recognized instance of pursuit-evasion on a graph is the Parsons problem[17].The idea behind the Parsons problem,also known as the edge-searching problem,is to determine a sequence of moves for the pursuers that can detect all intruders in a graph using the least number of pursuers.A move consists of either placing or removing a pursuer on a vertex,or sliding it along an edge.A vertex is considered guarded as long as it has at least one pursuer on it,and any evader located therein or attempting to pass through will be detected.A sliding move detects any evader on an edge.The visibility-based pursuit-evasion problem was proposed by Suzuki and Yamashita[24]as a geometric formulation of the graph-based problem and can be viewed as an extension of the watchman route problem[1],in which the objective is to compute the shortest path that a guard should take to patrol an entire area populated with obstacles,given only a map of the area.A.Single PursuerThe capture condition for the general visibility-based pursuit-evasion problem[4]is deﬁned as having an evader lie within a pursuer’s capture region.There has been substantial research focused how the visibility-based pursuit-evasion problem changes when a robot has different capture regions. The k-searcher is a pursuer with k visibility beams[14], [24],the∞-searcher is a pursuer with omnidirectionalﬁeld of view[4],[16],and theφ-searcher is a pursuer whoseﬁeld-of-view[3]is limited to an angleφ∈(0,2π].Note that all of these approaches consider evaders with unbounded speed. Others have studied scenarios where there are additional constraints,such as the case of curved environments[13],an unknown environment[20],a maximum bounded speed for the pursuer[26],or constraints similar to those of a typical bug algorithm[18].B.Multiple PursuerAs a result of the problem complexity,there is a wide range of literature with differing techniques attempting to solve the multi-robot visibility-based pursuit-evasion prob-lem.One technique organizes the pursuers into teams,whose joint sensing capability are a set of moving lines,each of which is spanned between obstacles.By using these teams of robots as sweep lines,the authors guarantee de-tection of the evaders[12].Other researchers have used a mixed integer linear programming approach to solve a multi-pursuer visibility-based pursuit-evasion problem[25]. Another approach involves maintaining complete coverage of the frontier[2].There are other variants of the pursuit-evasion problem where the pursuers are teams of unmanned aerial vehicles[11].III.P ROBLEM S TATEMENTPortions of this section appear in the authors’prior work[23]and are included here for completeness.A.Representing the environment,evaders,and pursuers1)The environment:The environment is a polygonal free space,deﬁned as a closed and bounded set F⊂R2,with a polygonal boundary∂F.The environment is composed of m vertices.2)The evader:The evader is modeled as a point in F that can translate within the environment.Let e(t)∈F denote the position of the evader at time t≥0.The path e is a continuous function e:[0,∞)→F,in which the evader is capable of moving arbitrarily fast(i.e.aﬁnite,unbounded speed)within F.Note that,by assuming that there is a single evader,we have not sacriﬁced any generality.If the pursuers can guarantee the capture of a single evader,then the same strategy can locate multiple evaders,or conﬁrm that no evaders exist.3)The pursuers:A collection of n identical pursuers cooperatively move to locate the evader.We assume that the pursuers know F,and that they are centrally coordinated. Therefore,from a given collection of starting positions,the pursuers’motions can be described by a continuous functionFig.2:An environment with two pursuers and three shadows. p:[0,∞)→F n,so that p(t)∈F n denotes the joint conﬁguration of the pursuers at time t≥0.The function p,which our algorithm generates,is called a joint motion strategy for the pursuers.We use the notation p i(t)∈F to refer to the position of pursuer i at time t.Likewise,x i(t) and y i(t)denote the horizontal and vertical coordinates of p i(t).Without loss of generality,we assume that the pursuers move with maximum speed1.Each pursuer carries a sensor that can detect the evader. The sensor is omnidirectional and has unlimited range,but cannot see through obstacles.For any point q∈F,let V(q) denote the visibility region at point q,which consists of the set of all points in F that are visible from point q.That is, V(q)contains every point that can be connected to q by a line segment in F.Note that V(q)is a closed set.B.Capture conditionsThe pursuers’goal is to guarantee the capture of the evader for any continuous evader trajectory.Deﬁnition A joint motion strategy is a solution strategy if, for any continuous evader trajectory e:[0,∞)→F, there exists some time t and some pursuer i such that e(t)∈V p i(t) .IV.S HADOWSThe key difﬁculty in locating our evader is that the pursuers can not,in general,see the entire environment at once.This section contains some deﬁnitions for describing and reasoning about the portion of the environment that is not visible to the pursuers at any particular time.Deﬁnition The portion of the environment not visible to the pursuers at time t is called the shadow region S(t),and deﬁned asS(t)=F− i=1,...,n V p i(t) .Note that the shadow region may contain zero or more nonempty path-connected components,as seen in Figure2. Deﬁnition A shadow is a maximal path connected compo-nent of the shadow region.Notice that S(t)is the union of the shadows at time t.The important idea is that the evader,if it has not been captured, is always contained in exactly one shadow,in which it can move freely.As the pursuers move,the shadows can change in any of four ways,called shadow events.•Appear:A new shadow can appear,when a previously visible part of the environment becomes hidden.•Disappear:An existing shadow can disappear,when one or more pursuers move to locations from which that region is visible.•Split:A shadow can split into multiple shadows,when the pursuers move so that a given shadow is no longer path-connected.•Merge:Multiple existing shadows can merge into a single shadow,when previously disconnected shadows become path-connected.These events were originally enumerated in the context of the single-pursuer version of this problem[4]and examined more generally by Yu and LaValle[28].A.Shadow LabelsFor our pursuit-evasion problem,the crucial piece of information about each shadow is whether or not the evader might be hiding within it.Deﬁnition A shadow s is called clear at time t if,based on the pursuers’motions up to time t,it is not possible for the evader to be within s without having been captured.A shadow is called contaminated if it is not clear.That is,a contaminated shadow is one in which the evader may be hiding.Notice that,since the evader can move arbitrarily quickly, the pursuers cannot draw any more detailed conclusion about each shadow than its clear/contaminated status;if any part of a shadow might contain the evader,then the entire shadow is contaminated.Therefore,our algorithm tracks the clear/contaminated status of each shadow.Each time a shadow event occurs, the labels can be updated based on worst case reasoning.•Appear:New shadows are formed from regions that had just been visible,so they are assigned a clear label.•Disappear:When a shadow disappears,its label is discarded.•Split:When a shadow splits,the new shadows inherit the same label as the original.•Merge:When shadows merge,the new shadow is as-signed the worst label of any of the original shadows’labels.That is,a shadow formed by a merge event is labeled clear if and only if all of the original shadows were also clear.Notice in particular that,if all of shadows are clear,then we can be certain the evader has been seen at some point. The result of this reasoning is that we can connect the shadow labels to our goal ofﬁnding a solution strategy.A pursuer strategy is a solution strategy if and only if,after its execution,all of the shadows are clear.bel DominanceThe following provides some insight to the hierarchy of preferable shadow rmally,we prefer one shadowlabel to another if in addition to having the same shadows labelled as cleared,there are additional shadows in the label that are also labelled as cleared.This allows us to say that one shadow label dominates another shadow label.Deﬁnition Given two shadow labels corresponding to a shadow region S,we say that a label l dominates a label l′if the following condition holds:∀s∈S If l′s=clear then l s=clear This relation is useful because our algorithm discards any shadow labels that are dominated by another shadow label reachable at the same pursuer conﬁguration.V.S AMPLE-G ENERATED P URSUIT-E VASION G RAPH This section introduces the primary data structure used in our algorithm.We begin by describing the graph’s structure and also elaborate on a non-trivial graph operation.A.Graph StructureThe Sample-Generated Pursuit-Evasion Graph(SG-PEG) is a rooted directed graph whose vertices represent joint pursuer conﬁgurations.A vertex in the SG-PEG contains1)a joint pursuer conﬁguration(denoted jpc),and2)the set of non-dominated shadow labels reachable byfollowing a path from the root,through the graph,to that conﬁguration.For an edge to exist between any two vertices in the SG-PEG there must be a line segment in F n that connects the joint pursuer conﬁguration at the source vertex with the joint pursuer conﬁguration at the target vertex.Given an arc of the SG-PEG,e=(x,y),the edge stores a mapping from the reachable shadow labels in x to the corresponding shadow labels in y.The operations available to a SG-PEG graph are A DD-V ERTEX and A DD E DGE.These operations differ from the corresponding operations on a standard graph because of the book-keeping needed to keep track of the reachable shadow labels.The A DD V ERTEX operation is trivial,but details concerning the A DD E DGE operation appear in the next section.B.Edge CreationWhen a new connection is established between a source and target vertex in the SG-PEG,the source’s reachable shadow labels are used to update the target’s reachable labels (Algorithm1).In this section we discuss the shadow label update criterion,the update label subroutine,and the process of adding a new reachable label to a vertex.1)Computing a New Label:In the authors’prior work [23],we provided a family of polynomials that capture where critical changes can occur to the region of the environment hidden from the pursuers.Although complete,the quantity and complexity of the polynomials(there are O n3m3 polynomials,where n corresponds to the number of pursuersand m corresponds to the number of environment vertices)in this family makes the task of analytically identifying where Algorithm1A DD E DGE(v,v′)Input:a source vertex v and a target vertex v′1:for each label in v’s reachable set do2:updated←C OMPUTE L ABEL(v.jpc,label,v′.jpc) 3:A DD R EACHABLE(v′,updated)these changes occur computationally expensive.Instead,we update the shadow labels numerically.The general idea is that if we partition the line segment connecting any two joint pursuer conﬁgurations in F n into a collection of evenly spaced joint pursuer conﬁgurations we can incrementally track the shadow changes.To ensure that all of the shadow events are captured there must be at least one sample capable of capturing each successive shadow event while traversing along the segment.The computation of a new shadow label(Algorithm2) takes as input two joint pursuer conﬁgurations,a source and target,and a shadow label corresponding to the shadow region at the source conﬁguration.The output is the shadow label that results from the pursuers moving from the source conﬁguration to the target conﬁguration given the initial shadow label.Figure3illustrates this process.Initially,there are two contaminated shadows.As the pursuers move to the target conﬁguration,a shadow appears as the pursuers move to the right(a cleared shadow).As the pursuers reach the target conﬁguration the central shadow disappears.We begin by partitioning(Algorithm2line2)the segment connecting the source and target conﬁgurations in F n into aﬁnite collection of evenly spaced joint pursuer conﬁgura-tions.We then loop through this collection of joint pursuer conﬁgurations,updating the shadow label along the way, returning theﬁnal label of the sequence.The process of computing the new shadow labels for our discretized segments appears in Algorithm2lines4-11.The process starts by computing the shadow regions of both the source and target conﬁgurations.We initialize the label corresponding to the target conﬁguration as all cleared.We check all of the shadows in the shadow region of the goal conﬁguration for an intersection with contaminated shadows belonging to the shadow region of the source conﬁguration. If an intersection with a contaminated shadow occurs then the corresponding shadow in the target conﬁguration is also labelled as contaminated.2)Adding a Reachable Label:Theﬁnal step involves adding the newly computed shadow label to the target vertex (Algorithm3).It may also be the case that the individual shadows of the new label are all cleared,in which case a solution has been found.If the target vertex contains a shadow label in its set of reachable labels that dominates the new shadow label,then the new label does not contribute any new information and we return.Similarly,if there are labels in the vertex’s set of reachable labels that are dominated by the new shadow label then those labels are removed.If the new shadow label is not dominated and is not a solution strategy then we add the new shadow label to the vertex’sBefore During AfterFig.3:An illustration of the update step.Initially there are two contaminated shadows(red).After running the U PDATE method,there is a cleared shadow(green)and a contaminated shadow(red).Algorithm2C OMPUTE L ABEL(p,l,p′)Input:a starting conﬁguration p,starting label l,anda goal conﬁguration p′1:label←l2:<p1,...,p k>←D ISCRETIZE(p,p′)3:for each p i,p i+1where i<k do4:oldshadows←S HADOW R EGION(p)5:newshadows←S HADOW R EGION(p′)6:newlabel←0···0⊲initially all cleared 7:for each s′in newshadows do8:for each s in oldshadows do9:if label s=1and s′intersects s then 10:newlabel s′←111:label←newlabel12:return labelAlgorithm3A DD R EACHABLE(v,l)Input:a SG-PEG vertex v and a label l1:function A DD R EACHABLE(v,l)2:if v contains a label that dominates l then return 3:add l to v as a reachable label4:delete labels in v dominated by l5:if A LL C LEAR(l)then6:Output Solution v⊲Is l a solution? 7:for each out in Neighbors(v)do8:newlabel←U PDATE(v.jpc,l,out.jpc)9:A DD R EACHABLE(out,newlabel)reachable set.This label now permeates the graph recursively via the vertex’s outgoing edges.A label is calculated for each of the vertex’s neighbors,and if this label is added to the neighbors reachable set,then the process repeats itself.The process ends when no additional reachable labels are found. Note that if a vertex does not belong to the same connected component as the root vertex then its set of reachable labels is empty.Because of the recursive nature of Algorithm3, a vertex that serves as a bridge between the connected component containing the root vertex and another connected component will cause the reachable data to permeate through the SG-PEG.Algorithm4S OLVE(p,F,A)Input:a starting conﬁguration p,an environment F,and an abstract sampler A1:A DD V ERTEX(p,{0···0})2:while a solution has not been found do3:s←A.G ET S AMPLE()4:x←A DD V ERTEX(s)5:for each y in SG-PEG vertices do6:if(xy⊂F n)andlength(x,y)<maxlength andcycleLength(x,y)>mincycle then7:A DD E DGE(x,y)⊲Digraph edge 8:A DD E DGE(y,x)⊲Digraph edge 9:return E XTRACT S OLUTION(solution)VI.A LGORITHMIn this section we detail how our algorithm uses a SG-PEG to search for a pursuer solution strategy.Our algorithm (Algorithm4)begins by creating a SG-PEG vertex.This vertex’s joint pursuer conﬁguration is the initial joint pur-suer conﬁguration supplied to our algorithm and it’s set of reachable shadow labels contains only a single label whose shadows are all contaminated.This is the root vertex of our SG-PEG.We then proceed by obtaining samples in F n,checking these samples for potential connections with existing vertices in the SG-PEG graph,and update the SG-PEG where necessary when edges are created.A.Abstract SamplerOur main search algorithm uses an abstract sampler to return a joint pursuer conﬁguration(Algorithm4line3). Deﬁnition An abstract sampler is a joint probability density function whose continuous random variables are the pur-suers’positions in F.The only functionality that we require an abstract sampler to have is the ability to generate a point in F n.The beneﬁt of using an abstract sampler is that our algorithm is not dependent on a speciﬁc sampler to generate a solution strategy.This allows us to choose samplers that efﬁciently explore F n.Note that the goal of catching the evaders means that the best sampling strategies may differ from those used in traditional motion planning algorithms.However,forour algorithm to be probabilistically complete,the abstract sampler must have a support equal to F n(Section VI-D). We demonstrate the feasibility of using an abstract sample generator in our algorithm by providing simulation results that utilize various sample generators(Section VII).B.Edge CriteriaIn this section we discuss the constraints used in our main algorithm that determine whether an edge should connect two vertices(Algorithm4line6).The three constraints can be categorized as visibility,edge length,and cycle length constraints.1)Visibility Condition:The visibility condition states that for two vertices to share a pair of directed edges,the vertices corresponding joint pursuer conﬁgurations must be mutually visible to one another.This corresponds to the i th pursuer of one conﬁguration residing within the visibility region of the i th pursuer in a neighboring conﬁguration.Another way of interpreting this constraint is that only straight line motions are permitted between corresponding pursuers in neighboring vertices.This constraint prevents the generation of strategies in which the pursuers collide with obstacles.2)Edge Length:To limit the amount of time spent computing the reachable data when an edge is added in the SG-PEG we place a constraint on the length of the segment connecting the vertices joint pursuer conﬁgurations in F n. The idea is that given two joint conﬁgurations that are far apart,requiring multiple intermediary vertices as opposed to a single long connection is preferred.The intermediary vertices provide additional opportunities for any potential subsequent samples to become connected.3)Minimum Cycle Length:To avoid an oversaturation of edges we enforce a minimum cycle length in the SG-PEG.The intuition is that if a large number of samples in F n that are relatively close together,a large amount of resources could potentially be used computing all of the nearby transitions without necessarily revealing any new information.This optimization is aimed at minimizing the number of samples between which no shadow events occur.C.Search for a solution strategyThe intuition is that given an initial joint pursuer conﬁgu-ration,we assume that all the shadows in the shadow region are contaminated.We then build a SG-PEG using an abstract sampler to select new points in F n.Since we maintain the reachable shadow labels during the construction of the SG-PEG,we know that a solution strategy exists if we encounter a reachable shadow label that is completely cleared.At that point we use the reachable data stored in the vertices and the shadow label mappings stored in the edges to recover a solution by following those mappings back to the root.This solution should appear as a collection of vertices in the ing the joint pursuer conﬁgurations stored in the vertices as intermediary steps that the pursuers need to reach,we will have generated a joint motion strategy that is also a solution strategy.D.Probabilistic CompletenessFinally,we argue that under certain conditions,Algo-rithm4is probabilistically complete.Theorem1:If the abstract sampler has a support equal to F n,and there are no constraints on the edge length and cyclelength,then our algorithm is probabilistically complete.Thatis,the probability of our algorithmﬁnding a solution,if oneexists,tends to1as the number of samples goes to inﬁnity. Proof Sketch:The argument proceeds in the same fashion as the probabilistic completeness proof for PRM presentedby Kavraki,Kolountzakis,and Latombe[9].The only signif-icant difference is that,instead of considering the clearancebetween a solution strategy and the obstacle boundaries,wemust consider the clearance from the critical boundaries atwhich shadow events that are not part of theﬁnal solutionstrategy would occur.VII.S IMULATION R ESULTSWe implemented our algorithm in simulation and providesome results for three different environments,using threedifferent sample generators,and three different cycle con-straints.The environments(Figure4)all require at leasttwo pursuers to generate a solution strategy.As such wehave deployed two pursuers to test our algorithm.The threedifferent sample generators have the following behavior:•SG1-Returns a uniform sample in F n.This is a baseline sample generator that produces independentand identically distributed samples in F n.This samplegenerator satisﬁes the completeness constraint.•SG2-Chooses samples such that no two pursuers are mutually visible.By ensuring that the pursuers can not see one another,we attempt to maximize exploration by generating samples where the pursuers’visibility regions don’t overlap.Note that this sample generator does not satisfy the completeness constraint.•SG3-Selects an existing SG-PEG vertex,and for each pursuer selects a new target position from the pursuer’s current visibility region.This is a local randomized sam-pler.By sampling within an existing SG-PEG vertex’s ﬁeld-of-view,we are essentially causing the search to “bloom”from the root vertex.This sample generator does not satisfy the completeness constraint.For each combination of environment,sample generator,and cycle constraints we ran10trials,each with a uniquestarting position.The simulations were implemented in C++on a machine running Ubuntu12.0464-bit with an IntelCore2Duo E8400processor and4GB of RAM.Eachsimulation was given a maximum computation time limit of1200seconds.If the algorithm could not generate as solution strategy within the allotted time,we assumed that it failed. The cycle constraints represent the extremes and one intermediary constraint.By not allowing any cycles,the SG-PEG has a tree structure,and may encounter environments where this limitation prevents our algorithm from generating a solution strategy.The other extreme has no constraint on the cycles.This means that if the samples are close together,。

贝叶斯网络结构学习总结

贝叶斯⽹络结构学习总结完备数据集下的贝叶斯⽹络结构学习：基于依赖统计分析的⽅法—— 通常利⽤统计或是信息论的⽅法分析变量之间的依赖关系，从⽽获得最优的⽹络结构对于基于依赖统计分析⽅法的研究可分为三种：基于分解的⽅法（V结构的存在）Decomposition of search for v-structures in DAGsDecomposition of structural learning about directed acylic graphsStructural learning of chain graphs via decomposition基于Markov blanket的⽅法Using Markov blankets for causal structure learningLearning Bayesian network strcture using Markov blanket decomposition基于结构空间限制的⽅法Bayesian network learning algorithms using structural restrictions(将这些约束与pc算法相结合提出了⼀种改进算法，提⾼了结构学习效率)（约束由Campos指出包括1、⼀定存在⼀条⽆向边或是有向边 2、⼀定不存在⼀条⽆向边或有向边 3、部分节点的顺序）常⽤的算法：SGS——利⽤节点间的条件独⽴性来确定⽹络结构的⽅法PC——利⽤稀疏⽹络中节点不需要⾼阶独⽴性检验的特点，提出了⼀种削减策略:依次由0阶独⽴性检验开始到⾼阶独⽴性检验，对初始⽹络中节点之间的连接进⾏削减。

此种策略有效地从稀疏模型中建⽴贝叶斯⽹络，解决了SGS算法随着⽹络中节点数的增长复杂度呈指数倍增长的问题。

TPDA——把结构学习过程分三个阶段进⾏:a)起草(drafting)⽹络结构，利⽤节点之间的互信息得到⼀个初始的⽹络结构;b)增厚(thickening)⽹络结构，在步骤a)⽹络结构的基础上计算⽹络中不存在连接节点间的条件互信息，对满⾜条件的两节点之间添加边;。

定量药理学名词中英对照表

English中文absolute prediction error(s) (APE)绝对预测误差absorption, distribution, metabolism, elimination (ADME)吸收、分布、代谢、消除active transport主动转运adaptive design自适应性设计additive error加和性误差adherence依从性administration给药affinity亲和力agonist激动剂allometric scaling异速生长antagonist拮抗剂area under curve (AUC)曲线下面积assumptions假设auto-induction自诱导backward elimination逆向剔除法base model基础模型baseline基线below the limit of quantification (BLQ)低于定量下限between-subject variability (BSV)个体间变异bias偏差biliary clearance胆汁清除率bioavailability生物利用度bioequivalence生物等效性biomarker生物标志物biopharmaceutics classification system (BCS)生物药剂学分类系统blood血body mass index (BMI)体质指数body surface area (BSA)体表面积bolus推注bootstrap自举法bottom-up appraoch自下而上的模式capacity-limited metabolism能力限制型代谢categorical data分类数据catenary compartment model链式模型causality因果chi-square test卡方检验clearance清除率Clinical trial simulation临床试验模拟clinical utility index临床效用指数Cmax峰浓度coefficient of variation (CV)变异系数Compartmental analysis房室模型分析competitive inhibition竞争性抑制compliance依从性concomitant medication effect联合用药效应condition number条件数conditional probability条件概率conditional weighted residuals (CWRES)条件加权残差confidence interval置信区间constitutive model本构模型continuous data连续数据convergence收敛correlation相关correlation coefficient相关系数correlation matrix相关矩阵count data计数数据covariacne matrix协方差矩阵covariance协方差covariate evaluation协变量评价covariate model协变量模型creatinine clearance肌酐清除率cross-over design交叉设计data analysis plan数据分析计划dataset assembly/construction数据集建立dataset specification file数据库规范文件degrees of freedom自由度dependent variable (DV)因变量determinant行列式deterministic identifiability确定性可识别性deterministic simulation确定性模拟diagonal matrix对角矩阵dichotomous二分类direct-effect model直接效应模型discrete离散disease progression疾病进程disease-modifying effect疾病缓解效应dose dependence剂量依赖性dose-normalized concentrations剂量归一化浓度double-blind双盲drug accumulation药物蓄积drug-drug interaction药物-药物相互作用duration of infusion输注持续时间efficacy功效eigenvalues特征值empirical Bayesian estimates (EBEs)经验贝叶斯估计endogenous内源性enterhepatic circulation肝肠循环estimate估计值estimation求参exogenous外源性exploratory data analysis (EDA)探索性数据分析exponential指数型external validation外部验证extrapolation外推extravascular administration血管外给药fasted禁食fed进食first in human (FIH) trial首次人体试验first-order absorption一级吸收first-order conditional estimation method (FOCE)一阶条件估计法first-order method (FO)一阶评估法first-pass effect首过效应Fisher information matrix Fisher信息矩阵fixed effect固定效应flip-flop翻转forward selection前向选择fraction of unbound (fu)游离分数full agonist完全激动剂gastric emptying胃排空generic products仿制药genetic polymorphism遗传多态性genome-wide association study (GWAS)全基因组关联研究genotype基因型global minimum全局最小值global sensitivity analysis全局敏感性分析glomerular filtration rate (GFR)肾小球滤过率goodness of fit拟合优度gradient梯度half maximal inhibitory concentration (IC50)半数抑制浓度half-life半衰期hepatic clearance肝清除率hierarchical层级homeostasis稳态homoscedasticity方差齐性hysteresis滞后identity matrix单位矩阵ill-conditioned matrix病态矩阵immunogenicity免疫原性in silico经由电脑模拟in situ原位in vitro体外in vivo体内independent variable自变量indirect response model间接反应模型individual parameter estimates个体参数估计individual prediction (IPRED)个体预测值individual residuals (IRES)个体残差individual weighted residuals (IWRES)个体加权残差infusion输注initial estimate起始参数估计inter-individual variability (IIV)个体间变异internal validation内部验证inter-occasion variability场合间变异interpolation插值intestinal absorption肠道吸收intra-individual variability个体内变异intramuscular administration (i.m.)肌肉注射intravenous administration (i.v.)静脉给药intrinsic clearance内在清除率inverse agonist反向激动剂inverse of matrix逆矩阵isobologram等效线图Jacobian matrix雅可比矩阵lag time滞后时间large-scale systems model大型系统模型lean body weight瘦体重level 1 random effect (L1)一级随机效应level 2 random effect (L2)二级随机效应ligand-receptor binding配体-受体结合likelihood ratio test似然比检验linear models线性模型linear pharmacokinetics线性药物动力学local minimum局部最小值local sensitivity analysis局部敏感性分析locally weighted scatterplot smoothing (LOWESS)局部加权散点平滑法logistic regression Logistic回归logit transform Logit变换log-normal distribution对数正态分布log-transformation对数变换maintenance dose维持剂量marginal probability边际概率mean均值mean absolute prediction error percent (MAPE)平均绝对预测误差百分比mean prediction error (MPE)平均预测误差mean residence time (MRT)平均滞留时间mean squared error (MSE)均方误差mechanism-based inhibition基于机制的抑制median中位数Michaelis-Menten constant米氏常数Michaelis-Menten kinetics米氏动力学missing dependent variable (MDV)缺失应变量mixed effect混合效应mixture models混合模型model diagnostic plots模型诊断图model evaluation模型评价model misspecification模型错配model specification file (MSF)模型规范文件model validation模型验证Model-based drug development基于模型的药物研发moment矩Monte Carlo simulation蒙特卡洛模拟multivariate linear regression多元线性回归negative feedback负反馈nested嵌套Non-compartmental analysis非房室模型分析noncompetitive inhibition非竞争性抑制nonlinear mixed effect models (NONMEM)非线性混合效应模型nonlinear pharmacokinetics非线性药物动力学normal distribution正态分布normalized prediction distribution errors (NPDE)归一化预测分布误差numerical predictive check (NPC)数值预测性能检查objective function value (OFV)目标函数值observation观测occupancy占有occupational model受体占有模型one-/two-compartment model一/二室模型onset of effect起效operational model操作模型optimal sampling最优采样Optimal study design优化试验设计oral口服ordered data有序数据outlier离群值parallel design 平行设计partial agonist部分激动剂peak concentration峰浓度perfusion灌注permeability渗透性Pharmacodynamics药效动力学Pharmacogenomics药物基因组学Pharmacokinetics 药物动力学Pharmacometrics定量药理学phase I reaction第一相反应phase II reaction第二相反应phenotype表型Physiologically based pharmacokinetics (PBPK)生理药物动力学piecewise linear models分段线性模型placebo安慰剂plasma血浆Poisson distribution泊松分布Poisson regression泊松回归population pharmacokinetics群体药物动力学positive feedback正反馈post hoc事后posterior distribution后验分布posterior predictive check (PPC)后验预测性能检查posterior probability后验概率potency效价强度power function幂函数precision精密度pre-clinical study临床前研究prediction (PRED)群体预测值prediction error (PE)预测误差prior distribution先验分布prodrug前药proof of concept study概念验证研究proportional error比例型误差Q-Q plot分位图quality assurance (QA)质量保证quality control (QC)质量控制random effect随机效应randomisation 随机化rate constant速率常数rate-limiting step限速步骤reference group对照组relative bioavailability相对生物利用度relative standard deviation (RSD)相对标准偏差relative standard error (RSE)相对标准误renal clearance肾清除率reparameterization重新参数化repeat dose重复剂量resampling重采样residual (RES)残差residual unexplained variability (RUV)残留不明原因的变异·rich sampling密集采样robust鲁棒性root mean square error (RMSE)均方根误差rounding errors舍入误差saturable可饱和的semi-logarithmic plot半对数图shirinkage收缩signalling transduction信号转导simulation模拟single dose单剂量singular奇异sparse sampling稀疏采样standard error (SE)标准误steady state (SS)稳态stochastic simulation随机模拟stratification分层structural identifiability结构可识别性subcutaneous administration (s.c.)皮下注射superposition叠加surrogate endpoint替代终点survival analysis生存分析symptomatic effect对症疗效synergism协同作用Systems pharmacology系统药理学target-mediated drug disposition靶点介导的药物处置therapeutic drug monitoring (TDM)治疗药物监测therapeutic index治疗指数time after dose (TAD)给药后时间time varying时间变化time-to-event analysis事件史分析tissue组织titration design滴定式设计tmax达峰时间tolerance耐受性top-down approach自上而下的模式total body weight总体重transit compartment model中转室模型transporter转运体transpose转置trough concentration谷浓度tubular reabsorption肾小管重吸收tubular secretion肾小管分泌turnover置换typical value paramters参数的群体典型值uncompetitive inhibition反竞争性抑制variance-covariance matrix方差协方差矩阵visual predictive check (VPC)可视化预测性能检查volume of distribution表观分布容积weighted residuals (WRES)加权残差well-stirred model充分搅拌模型within-subject variability个体内变异zero-order absorption零级吸收。

遥感专业英语词汇

1.remote sensing used in forestry 林业遥感2.restoration of natural resources 自然资源的恢复3.above ground biomass（AGB）地上生物量4. biogeochemical cycle 生物地球化学循环5.carbon cycle 碳循环6.stand structure 林分结构7.high deforestation rates 森林砍伐率8.carbon emissions 碳排放9.environmental degradation 环境恶化10.biomass estimation 生物量估测11.field data 样地数据12.remotely sensed data 遥感数据13.statistical relationships 统计相关性14.biomass estimation model 生物量估测模型15.stem diameter 径阶16.stem height 枝下高17.Tree height 树高18.Primary Forest 原始森林19.Successional Forest 次生林20.Endmember 端元21.canopy shadow 冠层阴影22.canopy closure 冠层郁闭度23.sampling strategy 抽样方案24.stratified random 分层随机25.endmembers 端元26.intrinsic dimensionality 固有维数27.phenological changes 物候变化28.Chlorophyll 叶绿素29.Absorption 吸收30.Amplitude 振幅31.spatial frequency 空间频率32.Fourier transformation 傅立叶变化33.Decomposition 分解34.grain gradient 纹理梯度35.allometric model 异速生长模型36.fresh weight 鲜重37.Dry weight 干重38.Multicollinearity 多重共线性39.Overfitting 过度拟合40.successional vegetation classification 次生林分类41.classifier 分类器42.supervised classification监督分类43.unsupervised classification 非监督分类44.fuzzy classifier method 迷糊分类法45. maximum likelihood classification 最大似然法分类46. minimum distance classification 最小距离法分类47. Bayesian classification 贝叶斯分类48. Image analysis 图像分析49. feature extraction 特征提取50. feature analysis 特征分析51. pattern recognition 模式识别52. texture analysis 纹理分析53. ratio enhancement 比例增强54. edge detection 边缘检测55. image enhancement 影像增强56. reference data 参考数据57. auxiliary data 辅助数据58. principal component transformation 主成分变化59. histogram equalization 直方图均衡化60. image segmentation 图像分割61. geometric correction 几何校正62. geometric registration of imagery 几何配准63. radiometric correction 辐射校正64. atmospheric correction 大气校正65. synthetic aperture radar SAR 合成孔径雷达66. digital surface model, DSM 数字高程模型67. neighborhood method 邻近法68. least squares correlation 最小二乘相关69. illuminance of ground 地面照度70. geometric distortion 几何畸变71. mosaic 镶嵌72. pixel 像元73. quackgrass meadow 冰草草甸74. quagmire 沼泽地75. quantitative analysis 定量分析76. quantitative interpretation 定量判读77. radar echo 雷达回波78. radar image 雷达图像79. radar image texture 雷达图像纹理80. radiation 辐射81. rain intensity 降雨强度82. random distribution 随机分布83. random error 随机误差84. random sampling 随机抽样85. random variable 随机变量86. rare species 稀有种87. ratio method 比值法88. reafforestation 再造林89. reconnaissance survey 普查90. age structure 年龄结构91. recreation 休养92. afforestation 造林；植林93. recovery 再生94. abandoned land 弃耕地95. absorption 吸收〔作用〕96. climatic factor 气候因子97. reflected image 反射影像98. reforestation 森林更新99. regeneration cutting 更新伐100. regional remote sensing 区域遥感101. relative error 相对误差102. reliability 可靠性103. reversible process 可逆过程104. savanna forest 稀瘦原林105. heterogeneity 土壤差异性106. spectral resolution 光谱分辨率107. areal differentiation 地域分异108. substantial or systematic reproduction 实质性的或系统的繁殖109. initiated 开始110. converted 转变111. successional stages 演替系列112. uncertainties 不确定性113. soil fertility 土壤肥力114. land-use history 土地利用历史115. vegetation age 植被年龄116. spatial distribution 空间分布117. field measurements 样地测量118. characteristics 特征119. Saplings 树苗120. primary data 原始数据121. land cover 土地覆盖122. training sample 训练样本123. spectral signature 光谱特征124. spatial information 空间信息125. texture metrics 纹理度量126. texture measure 纹理测量127. data fusion 数据融合128. sensor 传感器129. multispectral data 多光谱数据130. panchromatic data 全色数据131. radar data 雷达数据132. classification algorithms 分类算法133. parametric 参数134. classification tree analysis 分类树135. K-nearest neighbor K近邻法136. Artifice alneural network (ANN) 神经网络137. per-pixel-based 基于像元的138. environmental features 环境要素139. preprocessing 预处理140. polarization 极化141. resampled 重采样142. image-to-image registration 影像到影像配准143. vegetation types 植被类型144. intensity-hue-saturation 亮度色度饱和度145. Brovey transform Brovey 变换146. Evaluated 评价147. error matrix 混淆矩阵148. Land use/cover classifation 土地利用/覆盖分类149. Misclassification 误分150. Classification accuracy 分类精度151. producer’s accuracy 生产者精度152. user’s accuracy 用户精度153. Optical multispectral image 光学多光谱影像154. optical sensor 光学传感器155. fusion techniques 融合技术156. uncertainty analysis 不确定性分析157. data saturation 数据饱和158. Parametric vs nonparametric algorithms 参数非参数算法159. global change 全球变化160. process model–based 基于模型的过程161. empirical model–based 基于经验的模型162. biomass expansion/conversion factor 生物量扩展/转换因子163. hyperspectral sensor 多光谱传感器164. radar data 雷达数据165. belowground biomass 地下生物量166. aboveground biomass 地上生物量167. GIS-based 基于GIS的168. ecosystem models 生态模型169. photosynthesis 光合作用170. anthropogenic effects 人为影响171. homogeneous stands 均一的立地条件172. empirical regression models 经验回归模型173. variables 变量174. subcompartment 小斑175. DBH 胸径176. Spectral features 光谱特征177. Spatial features 空间特征178. Subpixel features 亚像元特征179. Active sensor 主动传感器180. Lidar data 雷达数据181. vegetation indices 植被指数182. biophysical conditions 生物物理条件183. soil fertilities 土壤特征184. near-infrared 近红外185. extracting textures 纹理提取186. mean 均值187. variance 方差188. homogeneity 同质性189. contrast 对比度190. entropy 信息熵191. mature forest 成熟林192. secondary forest 次生林193. nonphotosynthetic vegetation 非光合作用植被194. shade fraction 阴影分量195. soil fraction 土壤分量196. biomass density 生物量密度197. vegetation characteristics 植被特征198. species composition 树种组成199. growth phase 生长期200. spectral signatures 光谱信息201. moist tropical 热带雨林202. primary data 原始数据203. unstable 不稳定204. soil moisture 土壤水分205. horizontal vegetation structures 水平植被结构206. canopy cover 灌层覆盖度207. canopy height 灌层高度208. regression technique 回归技术209. interferometry technique 干涉技术210. terrain properties 地形要素211. backscattering coefficient 后向散射系数212. canopy elements 灌层要素213. backscattering values 散射值214. coherence of data 数据一致性215. the total coherence of a forest 森林的一致性216. forest transmissivity 森林透射率217. large scale biomass 大区域生物量218. Polarization Coherence Tomography 极化相干断层扫描219. filtering methods 滤波方法220. outliers 异常值221. stereo viewing 立体视觉222. laser return signal 激光反馈信号223. characterizing horizontal 水平特征224. characterizing vertical 垂直特征225. canopy structure 灌层结构226. biomass prediction 生物量预测227. height information 树高信息228. hypothetical example 假设样本229. mean height 平均树高230. univariate model 单变量模型231. metric 度量标准232. biomass accumulation 累计生物量233. categorical variables 绝对变量234. different source data 不同源数据235. DEM data DEM数据236. optimal variables 最佳变量237. expert knowledge 专家知识238. strong correlations 强相关239. weak correlations 弱相关240. stepwise regression analysis 逐步回归分析241. independent variables 独立变量242. Parametric algorithms 参数算法243. nonparametric algorithms 非参数算法244. linear regression models 线性回归模型245. nonlinearly related 非线性相关246. power models 指数模型247. nonlinear models 非线性模型248. random forest 随即森林249. support vector machine (SVM) 支持向量机250. Maximum Entropy 最大熵251. Simulation 仿真252. co-simulation 协同仿真253. normal distribution 正态分布254. spatial configuration 空间结构255. randomly setting 随机设置256. pixel estimation 像素估计257. sample variance 样本方差258. national forest inventory sample plot data 国家森林库存样地数据259. natural deciduous forests 自然落叶森林260. linear relationships 线性关系261. approximation 近似法262. mathematical functions 数学函数263. black-box model 黑箱模型264. iterating training 迭代训练265. root node 根节点266. internal nodes, 内部节点267. recursive partitioning algorithm 逐步分割算法268. stratified 分层269. terminal node 终端节点270. regression tree theory 回归树理论271. split 分割272. statistical learning algorithm 统计学习算法273. high-dimensional feature space 高维特征空间274. kernel 卷积核275. empirical averages 经验平均值276. subsections 分段277. Accurately estimating 精度评价278. relative errors 相对模糊279. global scales 全球尺度280. root mean square error (RMSE) 均方根误差281. correlation coefficient 相关系数282. systematic sampling 系统抽样283. data collection 数据收集284. subset 子集285. mapping forest biomass /carbon 生物量/碳储量制图286. sequestration 隔离287.forest management and planning 森林管理和规划288. allometric models 异速生长的模型289. representativeness 代表性290. lidar data 激光雷达数据291. vegetation structure gradient 植被结构梯度292. randomly perturbing 随机扰动293. north coordinates 北坐标294. coarser spatial resolution 粗分辨率295. grouping errors 分组误差296. Medium spatial resolution 中分辨影像297. population parameters 人口参数298. Mixed pixels 混合像元299. Mismatch 误差300. high spatial resolution images 高分辨率影像。

人工智能(AI)中英文术语对照表

人工智能（AI）中英文术语对照表目录人工智能（AI）中英文术语对照表 (1)Letter A (1)Letter B (2)Letter C (3)Letter D (4)Letter E (5)Letter F (6)Letter G (6)Letter H (7)Letter I (7)Letter K (8)Letter L (8)Letter M (9)Letter N (10)Letter O (10)Letter P (11)Letter Q (12)Letter R (12)Letter S (13)Letter T (14)Letter U (14)Letter V (15)Letter W (15)Letter AAccumulated error backpropagation 累积误差逆传播Activation Function 激活函数Adaptive Resonance Theory/ART 自适应谐振理论Addictive model 加性学习Adversarial Networks 对抗网络Affine Layer 仿射层Affinity matrix 亲和矩阵Agent 代理/ 智能体Algorithm 算法Alpha-beta pruning α-β剪枝Anomaly detection 异常检测Approximation 近似Area Under ROC Curve／AUC Roc 曲线下面积Artificial General Intelligence/AGI 通用人工智能Artificial Intelligence/AI 人工智能Association analysis 关联分析Attention mechanism注意力机制Attribute conditional independence assumption 属性条件独立性假设Attribute space 属性空间Attribute value 属性值Autoencoder 自编码器Automatic speech recognition 自动语音识别Automatic summarization自动摘要Average gradient 平均梯度Average-Pooling 平均池化Action 动作AI language 人工智能语言AND node 与节点AND/OR graph 与或图AND/OR tree 与或树Answer statement 回答语句Artificial intelligence,AI 人工智能Automatic theorem proving自动定理证明Letter BBreak-Event Point／BEP 平衡点Backpropagation Through Time 通过时间的反向传播Backpropagation/BP 反向传播Base learner 基学习器Base learning algorithm 基学习算法Batch Normalization/BN 批量归一化Bayes decision rule 贝叶斯判定准则Bayes Model Averaging／BMA 贝叶斯模型平均Bayes optimal classifier 贝叶斯最优分类器Bayesian decision theory 贝叶斯决策论Bayesian network 贝叶斯网络Between-class scatter matrix 类间散度矩阵Bias 偏置/ 偏差Bias-variance decomposition 偏差-方差分解Bias-Variance Dilemma 偏差–方差困境Bi-directional Long-Short Term Memory/Bi-LSTM 双向长短期记忆Binary classification 二分类Binomial test 二项检验Bi-partition 二分法Boltzmann machine 玻尔兹曼机Bootstrap sampling 自助采样法／可重复采样／有放回采样Bootstrapping 自助法Letter CCalibration 校准Cascade-Correlation 级联相关Categorical attribute 离散属性Class-conditional probability 类条件概率Classification and regression tree/CART 分类与回归树Classifier 分类器Class-imbalance 类别不平衡Closed -form 闭式Cluster 簇/类/集群Cluster analysis 聚类分析Clustering 聚类Clustering ensemble 聚类集成Co-adapting 共适应Coding matrix 编码矩阵COLT 国际学习理论会议Committee-based learning 基于委员会的学习Competitive learning 竞争型学习Component learner 组件学习器Comprehensibility 可解释性Computation Cost 计算成本Computational Linguistics 计算语言学Computer vision 计算机视觉Concept drift 概念漂移Concept Learning System /CLS概念学习系统Conditional entropy 条件熵Conditional mutual information 条件互信息Conditional Probability Table／CPT 条件概率表Conditional random field/CRF 条件随机场Conditional risk 条件风险Confidence 置信度Confusion matrix 混淆矩阵Connection weight 连接权Connectionism 连结主义Consistency 一致性／相合性Contingency table 列联表Continuous attribute 连续属性Convergence收敛Conversational agent 会话智能体Convex quadratic programming 凸二次规划Convexity 凸性Convolutional neural network/CNN 卷积神经网络Co-occurrence 同现Correlation coefficient 相关系数Cosine similarity 余弦相似度Cost curve 成本曲线Cost Function 成本函数Cost matrix 成本矩阵Cost-sensitive 成本敏感Cross entropy 交叉熵Cross validation 交叉验证Crowdsourcing 众包Curse of dimensionality 维数灾难Cut point 截断点Cutting plane algorithm 割平面法Letter DData mining 数据挖掘Data set 数据集Decision Boundary 决策边界Decision stump 决策树桩Decision tree 决策树／判定树Deduction 演绎Deep Belief Network 深度信念网络Deep Convolutional Generative Adversarial Network/DCGAN 深度卷积生成对抗网络Deep learning 深度学习Deep neural network/DNN 深度神经网络Deep Q-Learning 深度Q 学习Deep Q-Network 深度Q 网络Density estimation 密度估计Density-based clustering 密度聚类Differentiable neural computer 可微分神经计算机Dimensionality reduction algorithm 降维算法Directed edge 有向边Disagreement measure 不合度量Discriminative model 判别模型Discriminator 判别器Distance measure 距离度量Distance metric learning 距离度量学习Distribution 分布Divergence 散度Diversity measure 多样性度量／差异性度量Domain adaption 领域自适应Downsampling 下采样D-separation （Directed separation）有向分离Dual problem 对偶问题Dummy node 哑结点Dynamic Fusion 动态融合Dynamic programming 动态规划Letter EEigenvalue decomposition 特征值分解Embedding 嵌入Emotional analysis 情绪分析Empirical conditional entropy 经验条件熵Empirical entropy 经验熵Empirical error 经验误差Empirical risk 经验风险End-to-End 端到端Energy-based model 基于能量的模型Ensemble learning 集成学习Ensemble pruning 集成修剪Error Correcting Output Codes／ECOC 纠错输出码Error rate 错误率Error-ambiguity decomposition 误差-分歧分解Euclidean distance 欧氏距离Evolutionary computation 演化计算Expectation-Maximization 期望最大化Expected loss 期望损失Exploding Gradient Problem 梯度爆炸问题Exponential loss function 指数损失函数Extreme Learning Machine/ELM 超限学习机Letter FExpert system 专家系统Factorization因子分解False negative 假负类False positive 假正类False Positive Rate/FPR 假正例率Feature engineering 特征工程Feature selection特征选择Feature vector 特征向量Featured Learning 特征学习Feedforward Neural Networks/FNN 前馈神经网络Fine-tuning 微调Flipping output 翻转法Fluctuation 震荡Forward stagewise algorithm 前向分步算法Frequentist 频率主义学派Full-rank matrix 满秩矩阵Functional neuron 功能神经元Letter GGain ratio 增益率Game theory 博弈论Gaussian kernel function 高斯核函数Gaussian Mixture Model 高斯混合模型General Problem Solving 通用问题求解Generalization 泛化Generalization error 泛化误差Generalization error bound 泛化误差上界Generalized Lagrange function 广义拉格朗日函数Generalized linear model 广义线性模型Generalized Rayleigh quotient 广义瑞利商Generative Adversarial Networks/GAN 生成对抗网络Generative Model 生成模型Generator 生成器Genetic Algorithm/GA 遗传算法Gibbs sampling 吉布斯采样Gini index 基尼指数Global minimum 全局最小Global Optimization 全局优化Gradient boosting 梯度提升Gradient Descent 梯度下降Graph theory 图论Ground-truth 真相／真实Letter HHard margin 硬间隔Hard voting 硬投票Harmonic mean 调和平均Hesse matrix海塞矩阵Hidden dynamic model 隐动态模型Hidden layer 隐藏层Hidden Markov Model/HMM 隐马尔可夫模型Hierarchical clustering 层次聚类Hilbert space 希尔伯特空间Hinge loss function 合页损失函数Hold-out 留出法Homogeneous 同质Hybrid computing 混合计算Hyperparameter 超参数Hypothesis 假设Hypothesis test 假设验证Letter IICML 国际机器学习会议Improved iterative scaling/IIS 改进的迭代尺度法Incremental learning 增量学习Independent and identically distributed/i.i.d. 独立同分布Independent Component Analysis/ICA 独立成分分析Indicator function 指示函数Individual learner 个体学习器Induction 归纳Inductive bias 归纳偏好Inductive learning 归纳学习Inductive Logic Programming／ILP 归纳逻辑程序设计Information entropy 信息熵Information gain 信息增益Input layer 输入层Insensitive loss 不敏感损失Inter-cluster similarity 簇间相似度International Conference for Machine Learning/ICML 国际机器学习大会Intra-cluster similarity 簇内相似度Intrinsic value 固有值Isometric Mapping/Isomap 等度量映射Isotonic regression 等分回归Iterative Dichotomiser 迭代二分器Letter KKernel method 核方法Kernel trick 核技巧Kernelized Linear Discriminant Analysis／KLDA 核线性判别分析K-fold cross validation k 折交叉验证／k 倍交叉验证K-Means Clustering K –均值聚类K-Nearest Neighbours Algorithm/KNN K近邻算法Knowledge base 知识库Knowledge Representation 知识表征Letter LLabel space 标记空间Lagrange duality 拉格朗日对偶性Lagrange multiplier 拉格朗日乘子Laplace smoothing 拉普拉斯平滑Laplacian correction 拉普拉斯修正Latent Dirichlet Allocation 隐狄利克雷分布Latent semantic analysis 潜在语义分析Latent variable 隐变量Lazy learning 懒惰学习Learner 学习器Learning by analogy 类比学习Learning rate 学习率Learning Vector Quantization/LVQ 学习向量量化Least squares regression tree 最小二乘回归树Leave-One-Out/LOO 留一法linear chain conditional random field 线性链条件随机场Linear Discriminant Analysis／LDA 线性判别分析Linear model 线性模型Linear Regression 线性回归Link function 联系函数Local Markov property 局部马尔可夫性Local minimum 局部最小Log likelihood 对数似然Log odds／logit 对数几率Logistic Regression Logistic 回归Log-likelihood 对数似然Log-linear regression 对数线性回归Long-Short Term Memory/LSTM 长短期记忆Loss function 损失函数Letter MMachine translation/MT 机器翻译Macron-P 宏查准率Macron-R 宏查全率Majority voting 绝对多数投票法Manifold assumption 流形假设Manifold learning 流形学习Margin theory 间隔理论Marginal distribution 边际分布Marginal independence 边际独立性Marginalization 边际化Markov Chain Monte Carlo/MCMC马尔可夫链蒙特卡罗方法Markov Random Field 马尔可夫随机场Maximal clique 最大团Maximum Likelihood Estimation/MLE 极大似然估计／极大似然法Maximum margin 最大间隔Maximum weighted spanning tree 最大带权生成树Max-Pooling 最大池化Mean squared error 均方误差Meta-learner 元学习器Metric learning 度量学习Micro-P 微查准率Micro-R 微查全率Minimal Description Length/MDL 最小描述长度Minimax game 极小极大博弈Misclassification cost 误分类成本Mixture of experts 混合专家Momentum 动量Moral graph 道德图／端正图Multi-class classification 多分类Multi-document summarization 多文档摘要Multi-layer feedforward neural networks 多层前馈神经网络Multilayer Perceptron/MLP 多层感知器Multimodal learning 多模态学习Multiple Dimensional Scaling 多维缩放Multiple linear regression 多元线性回归Multi-response Linear Regression ／MLR 多响应线性回归Mutual information 互信息Letter NNaive bayes 朴素贝叶斯Naive Bayes Classifier 朴素贝叶斯分类器Named entity recognition 命名实体识别Nash equilibrium 纳什均衡Natural language generation/NLG 自然语言生成Natural language processing 自然语言处理Negative class 负类Negative correlation 负相关法Negative Log Likelihood 负对数似然Neighbourhood Component Analysis/NCA 近邻成分分析Neural Machine Translation 神经机器翻译Neural Turing Machine 神经图灵机Newton method 牛顿法NIPS 国际神经信息处理系统会议No Free Lunch Theorem／NFL 没有免费的午餐定理Noise-contrastive estimation 噪音对比估计Nominal attribute 列名属性Non-convex optimization 非凸优化Nonlinear model 非线性模型Non-metric distance 非度量距离Non-negative matrix factorization 非负矩阵分解Non-ordinal attribute 无序属性Non-Saturating Game 非饱和博弈Norm 范数Normalization 归一化Nuclear norm 核范数Numerical attribute 数值属性Letter OObjective function 目标函数Oblique decision tree 斜决策树Occam’s razor 奥卡姆剃刀Odds 几率Off-Policy 离策略One shot learning 一次性学习One-Dependent Estimator／ODE 独依赖估计On-Policy 在策略Ordinal attribute 有序属性Out-of-bag estimate 包外估计Output layer 输出层Output smearing 输出调制法Overfitting 过拟合／过配Oversampling 过采样Letter PPaired t-test 成对t 检验Pairwise 成对型Pairwise Markov property成对马尔可夫性Parameter 参数Parameter estimation 参数估计Parameter tuning 调参Parse tree 解析树Particle Swarm Optimization/PSO粒子群优化算法Part-of-speech tagging 词性标注Perceptron 感知机Performance measure 性能度量Plug and Play Generative Network 即插即用生成网络Plurality voting 相对多数投票法Polarity detection 极性检测Polynomial kernel function 多项式核函数Pooling 池化Positive class 正类Positive definite matrix 正定矩阵Post-hoc test 后续检验Post-pruning 后剪枝potential function 势函数Precision 查准率／准确率Prepruning 预剪枝Principal component analysis/PCA 主成分分析Principle of multiple explanations 多释原则Prior 先验Probability Graphical Model 概率图模型Proximal Gradient Descent/PGD 近端梯度下降Pruning 剪枝Pseudo-label伪标记Letter QQuantized Neural Network 量子化神经网络Quantum computer 量子计算机Quantum Computing 量子计算Quasi Newton method 拟牛顿法Letter RRadial Basis Function／RBF 径向基函数Random Forest Algorithm 随机森林算法Random walk 随机漫步Recall 查全率／召回率Receiver Operating Characteristic/ROC 受试者工作特征Rectified Linear Unit/ReLU 线性修正单元Recurrent Neural Network 循环神经网络Recursive neural network 递归神经网络Reference model 参考模型Regression 回归Regularization 正则化Reinforcement learning/RL 强化学习Representation learning 表征学习Representer theorem 表示定理reproducing kernel Hilbert space/RKHS 再生核希尔伯特空间Re-sampling 重采样法Rescaling 再缩放Residual Mapping 残差映射Residual Network 残差网络Restricted Boltzmann Machine/RBM 受限玻尔兹曼机Restricted Isometry Property/RIP 限定等距性Re-weighting 重赋权法Robustness 稳健性/鲁棒性Root node 根结点Rule Engine 规则引擎Rule learning 规则学习Letter SSaddle point 鞍点Sample space 样本空间Sampling 采样Score function 评分函数Self-Driving 自动驾驶Self-Organizing Map／SOM 自组织映射Semi-naive Bayes classifiers 半朴素贝叶斯分类器Semi-Supervised Learning半监督学习semi-Supervised Support Vector Machine 半监督支持向量机Sentiment analysis 情感分析Separating hyperplane 分离超平面Searching algorithm 搜索算法Sigmoid function Sigmoid 函数Similarity measure 相似度度量Simulated annealing 模拟退火Simultaneous localization and mapping同步定位与地图构建Singular Value Decomposition 奇异值分解Slack variables 松弛变量Smoothing 平滑Soft margin 软间隔Soft margin maximization 软间隔最大化Soft voting 软投票Sparse representation 稀疏表征Sparsity 稀疏性Specialization 特化Spectral Clustering 谱聚类Speech Recognition 语音识别Splitting variable 切分变量Squashing function 挤压函数Stability-plasticity dilemma 可塑性-稳定性困境Statistical learning 统计学习Status feature function 状态特征函Stochastic gradient descent 随机梯度下降Stratified sampling 分层采样Structural risk 结构风险Structural risk minimization/SRM 结构风险最小化Subspace 子空间Supervised learning 监督学习／有导师学习support vector expansion 支持向量展式Support Vector Machine/SVM 支持向量机Surrogat loss 替代损失Surrogate function 替代函数Symbolic learning 符号学习Symbolism 符号主义Synset 同义词集Letter TT-Distribution Stochastic Neighbour Embedding/t-SNE T –分布随机近邻嵌入Tensor 张量Tensor Processing Units/TPU 张量处理单元The least square method 最小二乘法Threshold 阈值Threshold logic unit 阈值逻辑单元Threshold-moving 阈值移动Time Step 时间步骤Tokenization 标记化Training error 训练误差Training instance 训练示例／训练例Transductive learning 直推学习Transfer learning 迁移学习Treebank 树库Tria-by-error 试错法True negative 真负类True positive 真正类True Positive Rate/TPR 真正例率Turing Machine 图灵机Twice-learning 二次学习Letter UUnderfitting 欠拟合／欠配Undersampling 欠采样Understandability 可理解性Unequal cost 非均等代价Unit-step function 单位阶跃函数Univariate decision tree 单变量决策树Unsupervised learning 无监督学习／无导师学习Unsupervised layer-wise training 无监督逐层训练Upsampling 上采样Letter VVanishing Gradient Problem 梯度消失问题Variational inference 变分推断VC Theory VC维理论Version space 版本空间Viterbi algorithm 维特比算法Von Neumann architecture 冯·诺伊曼架构Letter WWasserstein GAN/WGAN Wasserstein生成对抗网络Weak learner 弱学习器Weight 权重Weight sharing 权共享Weighted voting 加权投票法Within-class scatter matrix 类内散度矩阵Word embedding 词嵌入Word sense disambiguation 词义消歧。

sampling based method

sampling based method
"Sampling-based method" 是一种基于抽样的方法，通常用于从大规模数据集中选择有代表性的样本进行分析和处理。

这种方法的核心思想是通过从数据集中抽取一部分样本，而不是处理整个数据集，来减少计算和时间的复杂性。

通过选择合适的抽样方法，可以在保持数据集的基本特征和统计规律的前提下，有效地利用有限的计算资源和时间。

抽样方法可以根据不同的需求和应用场景进行选择。

一些常见的抽样方法包括简单随机抽样、分层抽样、系统抽样等。

简单随机抽样是从数据集中随机选择样本，每个样本被选中的概率相等。

分层抽样是将数据集按照某些特征或属性进行分层，然后从每个层中进行抽样。

系统抽样是按照一定的规律或间隔从数据集中选择样本。

抽样-based method 在数据分析、机器学习、统计推断等领域中有广泛的应用。

例如，在机器学习中，可以使用抽样方法对大规模数据集进行训练和测试，以减少计算负担和提高效率。

在统计推断中，可以通过抽样来估计总体参数或检验假设。

需要注意的是，抽样-based method 虽然可以减少计算负担，但在使用时需要考虑抽样误差和样本的代表性。

合理的抽样方法和样本大小的选择可以提高分析结果的准确性和可靠性。

贝叶斯复合回归模型工具包说明书

Package‘bayescopulareg’October12,2022Type PackageTitle Bayesian Copula RegressionVersion0.1.3Date2020-11-30Maintainer Ethan Alt<*****************.edu>Description Tools for Bayesian copula generalized linear models(GLMs).The sampling scheme is based on Pitt,Chan,and Kohn(2006)<doi:10.1093/biomet/93.3.537>.Regression parameters(including coefﬁcients and dispersion parameters)areestimated via the adaptive random walk Metropolis approach developed byHaario,Saksman,and Tamminen(1999)<doi:10.1007/s001800050022>.The prior for the correlation matrix is based on Hoff(2007)<doi:10.1214/07-AOAS107>. Depends R(>=3.6.0)License GPL(>=2)Imports Rcpp(>=1.0.3),statsLinkingTo Rcpp,RcppArmadillo,RcppDist,mvtnormRoxygenNote7.1.1Encoding UTF-8URL https:///ethan-alt/bayescopularegBugReports https:///ethan-alt/bayescopulareg/issuesNeedsCompilation yesAuthor Ethan Alt[aut,cre],Yash Bhosale[aut]Repository CRANDate/Publication2020-11-3017:10:06UTCR topics documented:bayescopulaglm (2)predict.bayescopulaglm (4)Index61bayescopulaglm Sample from Bayesian copula GLMDescriptionSample from a GLM via Bayesian copula regression es random-walk Metropolis to update regression coefﬁcients and dispersion parameters.Assumes Inverse Wishart prior on aug-mented data.Usagebayescopulaglm(formula.list,family.list,data,histdata=NULL,b0=NULL,c0=NULL,alpha0=NULL,gamma0=NULL,Gamma0=NULL,S0beta=NULL,sigma0logphi=NULL,v0=NULL,V0=NULL,beta0=NULL,phi0=NULL,M=10000,burnin=2000,thin=1,adaptive=TRUE)Argumentsformula.list A J-dimensional list of formulas giving how the endpoints are related to the covariatesfamily.list A J-dimensional list of families giving how each endpoint is distributed.See help(family)data A data frame containing all response variables and covariates.Variables must be named.histdata Optional historical data set for power prior onβ,φb0Optional power prior hyperparameter.Ignored if is.null(histdata).Must be a number between(0,1]if histdata is not NULLc0A J-dimensional vector forβ|φprior covariance.If NULL,sets c0=10000for each endpointalpha0A J-dimensional vector giving the shape hyperparameter for each dispersion parameter on the prior onφ.If NULL setsα0=.01for each dispersion parameter gamma0A J-dimensional vector giving the rate hyperparameter for each dispersion pa-rameter on the prior onφ.If NULL setsα0=.01for each dispersion parameter Gamma0Initial value for correlation matrix.If NULL defaults to the correlation matrix from the responses.S0beta A J-dimensional list for the covariance matrix for random walk metropolis on beta.Each matrix must have the same dimension as the corresponding regres-sion coefﬁcient.If NULL,uses solve(crossprod(X))sigma0logphi A J-dimensional vector giving the standard deviation on log(φ)for random walk metropolis.If NULL defaults to0.1v0An integer scalar giving degrees of freedom for Inverse Wishart prior.If NULL defaults to J+2V0An integer giving inverse scale parameter for Inverse Wishart prior.If NULL defaults to diag(.001,J)beta0A J-dimensional list giving starting values for random walk Metropolis on the regression coefﬁcients.If NULL,defaults to the GLM MLE phi0A J-dimensional vector giving initial values for dispersion parameters.If NULL.Dispersion parameters will always return1for binomial and Poisson mod-elsM Number of desired posterior samples after burn-in and thinningburnin burn-in parameterthin post burn-in thinning parameteradaptive logical indicating whether to use adaptive random walk MCMC to estimate pa-rameters.This takes longer,but generally has a better acceptance rateValueA named list.["betasample"]gives a J-dimensional list of sampled coefﬁcients as matrices.["phisample"]gives a M×J matrix of sampled dispersion parameters.["Gammasample"]gives a J×J×M array of sampled correlation matrices.["betaaccept"]gives a M×J matrix where each row indicates whether the proposal for the regression coefﬁcient was accepted.["phiaccept"] gives a M×J matrix where each row indicates whether the proposal for the dispersion parameter was acceptedExamplesset.seed(1234)n<-100M<-100x<-runif(n,1,2)y1<-0.25*x+rnorm(100)y2<-rpois(n,exp(0.25*x))formula.list<-list(y1~0+x,y2~0+x)family.list<-list(gaussian(),poisson())data=data.frame(y1,y2,x)##Perform copula regression sampling with default##(noninformative)priorssample<-bayescopulaglm(formula.list,family.list,data,M=M,burnin=0,adaptive=F)##Regression coefficientssummary(do.call(cbind,sample$betasample))##Dispersion parameterssummary(sample$phisample)##Posterior mean correlation matrixapply(sample$Gammasample,c(1,2),mean)##Fraction of accepted betascolMeans(sample$betaaccept)##Fraction of accepted dispersion parameterscolMeans(sample$phiaccept)predict.bayescopulaglmPredictive posterior sample from copula GLMDescriptionSample from the predictive posterior density of a copula generalized linear model regression Usage##S3method for class bayescopulaglmpredict(object,newdata,nsims=1,...)Argumentsobject Result from calling bayescopulaglmnewdata data.frame of new datansims number of posterior draws to take.The default and minimum is1.The maxi-mum is the number of simulations in object...further arguments passed to or from other methodsValuearray of dimension c(n,J,nsims)of predicted values,where J is the number of endpointsExamplesset.seed(1234)n<-100M<-1000x<-runif(n,1,2)y1<-0.25*x+rnorm(100)y2<-rpois(n,exp(0.25*x))formula.list<-list(y1~0+x,y2~0+x)family.list<-list(gaussian(),poisson())data=data.frame(y1,y2,x)##Perform copula regression sampling with default ##(noninformative)priorssample<-bayescopulaglm(formula.list,family.list,data,M=M)predict(sample,newdata=data)Indexbayescopulaglm,2predict.bayescopulaglm,46。

基于循序Ⅰ型删失数据的广义Pareto分布最优删失计划

基于循序Ⅰ型删失数据的广义Pareto分布最优删失计划程从华;程丽娟【摘要】文章讨论了循序删失计划下广义Pareto分布的统计推断问题并得到期望Fisher信息矩阵.利用期望Fisher信息矩阵,在三种不同准则下,讨论了最优删失计划的设计问题:如何确定参与寿命分析实验的元件个数,观测区间个数以及实验检测区间长度.最后,给出了完成寿命测试实验的一个具体算法,并且给出了一个具体实例来演示该算法.演示结果表明,文章提出的算法是可行和有效的.【期刊名称】《海南师范大学学报（自然科学版）》【年(卷),期】2017(030)004【总页数】5页(P391-395)【关键词】循序删失;信息矩阵;最优删失计划【作者】程从华;程丽娟【作者单位】肇庆学院数学与统计学院,广东肇庆526061;岭南师范学院数学与统计学院,广东湛江524048;岭南师范学院数学与统计学院,广东湛江524048【正文语种】中文【中图分类】O212.1广义Paretto分布最早由Pickands提出[1].随机变量X服从广义Pareto分布,如果它的概率密度函数(PDF)为(1)其中，μ,ξ∈R,σ∈(0,+∞).为了简单，重参数化参数，令这时，广义Pareto随机变量X的概率密度函数变为：f(x;α,λ)=αλ(1+λx)-(α+1).(2)对应的分布函数为：F(x;α,λ)=1-(1+λx)-α,x,α,λ>0.(3)其中，α和λ分别是形状参数和尺度参数.广义Pareto分布又被称为II型Pareto分布或是Lomax分布,这个分布具有单调递减失效率函数的特性.在可靠性研究中，如果投入寿命测试元件的寿命Y服从参数为v的指数分布，同时v是一个服从尺度参数和形状参数α的Gamma随机变量，则元件的寿命Y就是一个服从广义Pareto分布的随机变量.广义Pareto模型在极端事件分析中有着广泛的应用.比如，保险分析中的大额报单索赔问题以及可靠性分析中的失效时间建模问题都可以利用广义Pareto模型来进行建模分析. Harris研究了保修服务时间决策问题[2]. Davis和Feldstein利用广义Pareto模型研究了循序删失情形下的等效元件失效时间问题[3]. Hosking和Wallis研究了广义Pareto模型的参数和分位数估计问题[4]. Smith研究了非正则条件下的分布族参数最大似然估计问题[5]. Liang基于非参数经验贝叶斯方法讨论了广义Pareto分布的尺度参数估计问题[6]. Nigm等在两样本和随机样本容量条件下讨论了广义Pareto分布未知参数的贝叶斯区间估计问题[7].Wu等在循序删失数据情形下研究了广义Pareto分布参数的区间估计问题[8].寻找最优删失计划是一个近年来受到广泛关注的问题.在循序删失情形下，本文首先讨论广义Pareto分布未知参数的最大似然估计问题，并且给出广义Pareto模型在循序删失条件下的期望Fisher信息矩阵.其次利用期望Fisher信息矩阵，在三种不同准则下，讨论最优删失计划的设计问题.在寿命分析实验中，大多数寿命实验还会受到实验经费预算的约束.近几年来许多学者也对此进行了研究，比如：Tse 等[9]，Chen等[10]和 Wu等[11]. 在实验经费不超过给定数额条件下，讨论循序删失计划的最优设计问题. 针对循序I型区间删失特点，主要考虑三个问题,分别是如何确定参与寿命分析实验的元件个数，观测区间个数以及实验检测区间长度.最后给出完成寿命测试实验的一个具体算法，并且通过一个具体实例来演示本文的方法.1 期望信息矩阵为了计算未知参数的Fisher信息，需要以下的一些预备知识[12].Xi|Xi-1,Xi-2,…，X1，Ri-1,Ri-2,…，R1～B(Mi,qi),其中，服从二项分布，是第i步观测开始时未被移除的还在工作的元件个数，Mi+1=Mi-Xi-Ri,Ri=(Mi-Ri)×pi.通过计算，可以得到以下结论E(M1)=n,E(R1)=np1(1-q1),E(Xi)=E(Mi)qi,i=1,2,…，m,利用以上结论，可以得到期望Fisher信息矩阵E，且E可以表示为：(4)这里其中，h=log((1+λti-1)-α-(1+λti)-α).则未知参数(α λ)的最大似然估计的渐进协方差矩阵为(5)2 最优删失计划设计2.1 算法设计在实验成本约束条件下，这一小节讨论最优的实验设计问题.利用Ng等定义的如下三个最优准则来进行实验设计[13].(1)D-最优：最小化协方差矩阵的行列式，det(V(α,λ)(2)T-最优：最小化协方差矩阵的迹，tr(V(α,λ))=V11+V22.(3)F-最优：最大化参数最大似然估计期望Fisher矩阵的迹，tr(E(α,λ))=E11+E22.假定相邻的观测区间长度差都等于给定的长度t，同时假定有n个元件投入寿命测试实验，有m个观测时刻点，第i个观测区间的时间长度为it，i=1,2,…，m.同时假设以下实验设计参数.(a) 样本成本：令Cs是每一个参与测试的元件价格，则样本总成本为nCs.(b) 检测成本：令Ci是每一个参与测试的元件检测成本，则总检测成本为mCi.(c) 运转成本：令C0是每一个参与测试的元件在两次检测期间内的运转成本，则元件运转总成本为因此，寿命测试实验的总成本是：显而易见的是，每个实验准则都是n,m,t的函数，记为G(n,m,t).当实验总成本是给定参数Cb时，则约束条件变为：(6)因此，这个实验的最优设计可以表述为：min imize(max imize)G(n,m,t),subject to nCs+mCi+tC0≤Cb,n,m∈N,and t>0,其中，N是正整数.可以看到目标函数和约束条件都是非线性函数. 下面将利用非线性混合规划方法求解上述目标函数.非线性规划问题由Kamat和 Mesquita首先提出[14].关于非线性混合规划方法比较全面的知识可以参考Grossmann的介绍[15]. 对本文涉及的目标函数和具体问题,主要参考Taha的方法[16]. 基于上述介绍，给出如下算法. (Ⅰ)计算整数n的上界.在实验经费束条件下，由于m≥1，这个上界为其中[.]表示取整函数.(Ⅱ)令n=2，在给定n的条件下，计算m的上界.基于实验成本约束条件，m的上界为(Ⅲ)计算相邻观测区间长度的差.基于实验成本约束和条件，相邻观测区间时间长度差为：(Ⅳ)对于给定的n，计算函数G(n,m,tmn)的值.(Ⅴ)令函数F(n)(Ⅵ)令n=n+1，如果回到(Ⅱ)，否则进入(Ⅶ).(Ⅶ)计算最优函数G(n,m,tmn)的值，这里的),则(n*,m*,t*)就是我们寻找的最优删失计划.2.2 演示实例为了演示提出的算法，本节给出以下实例.利用Aggarwala提出的算法[12]，在样本容量n=45以及实验参数m=7,t=0.15,α=1.5的条件下，可以获得循序I型删失样本.事先设定删失计划参数(P1,P2,…，P6，P7)=(0.1,0.1,…,0.1,1)，则可以得到循序删失样本为X=(19,12,4,4,2,0,0)和R=(2,1,0,0,0,0,1).利用Dempster提出的EM算法[17]，获得参数的最大似然估计为：基于参数的最大似然估计和前一小节给出的算法，可以寻找到最优删失计划设计方案. 假设循序删失计划参数为：Cb=600,Cs=10,Ci=5,C0=2,则最优删失计划目标函数为min imize(max imize)G(n,m,t),subject to 10n+5m+m(m+1)t≤600,n,m∈N,and t>0.利用上节给出的算法，可以获得最优删失计划如下.D-最优：n*=55,m*=9,t*=0.0556.T-最优：n*=54,m*=11,t*=0.0379.F-最优：n*=56,m*=6,t*=0.2381.通过数值实验结果，可以发现无论是D-最优，T-最优还是F-最优，本文给出的方法均是可以实现的.但各方案实现的具体结果有所差异，其中D-最优方案和T-最优方案在结果上更为相近，F-最优方案则有较大差异.这一结果并不令人意外，因为F-最优方案利用的是期望Fisher信息矩阵，而D-最优方案和T-最优方案使用同一个协方差矩阵.因此在实践中，在小样本情形时，建议使用D-最优方案和T-最优方案,反之使用F-最优方案.参考文献：[1] Pickands J. Statistical inference using extreme order statistics[J]. The Annals of Statistics, 1975, 3(1): 119-131.[2] Harris C M. The Pareto distribution as a queue discipline[J]. Operations Research, 1968.16(2): 307-313.[3] Davis H T, Feldstein M L. The generalized Pareto law as a model for progroressively censored survival data[J]. Biometrika, 1979, 66(2): 299-306.[4] Hosking J R M,Wallis J R. Parameter and quantile estimation for the generalized Pareto distribution[J]. Technometrics, 1987, 29(3): 339-349. [5] Smith R L. Maximum likelihood estimation in a class of nonregularcases[J].Bimetrika, 1985, 72(1): 67-90.[6] Liang T C.Convergence rates for empirical Bayes estimation of the scaleparameter in a Pareto distribution[J]. Computational Statistics & Data Analysis , 1993, 16(1): 35-45.[7] Nigm A M, Al-Hussaini E K, Jaheen Z F. Bayesian two-sample predictionunder the Lomax model with fixed and random sample size[J]. Journal of Applied Statistics, 2003, 37(6): 527-536.[8] Wu S F. Interval estimation for the Pareto distribution based on the progressive type II censored sample[J]. Journal of Statistical Computation and Simulation, 2010, 80(4):463-474.[9] Tse S K, Yang C, Yuen H K. Design and analysis of survival data underan integrated type II interval censoring scheme[J]. Journal of Biopharmaceutical Statistics, 2002, 12(3): 333-345.[10] Chen J W, Li K H, Lam Y. Bayesian single and double variable sampling plans forthe Weibull distribution with censoring[J]. European Journal of Operational Research, 2007, 177(2): 1062-1073.[11] Wu S J, Huang S R. Optimal progressive group-censoring pans for exponential distribution in presence of cost constraint[J]. Statistical Papers, 2010, 51(2):4 31-443.[12] Aggarwala R. Progressive interval censoring: some mathematical results with applications to inference[J]. Communications in Statistics-Theory and Methods, 2001, 30(8): 1921-1935.[13] Ng H K T, Chan P S, Balakrishnan N. Optimal progressive censoring plan for the Weibull distribution[J]. Technometrics, 2004, 46(6): 470-481.[14] Kamat M P, Mesquita L.Nonlinear mixed integer programming[M]// Adeli H.Advanced in design optimization. London：Chapman & Hall Press, 1994.[15] Grossmann I E. Review of nonlinear mixed-integer and disjunctive programming techniques[J]. Optimization and Engineering, 1965, 3(3): 227-252.[16] Taha H A. Operations research: an introduction[M]. 5th ed.New York:Macmillan Press, 1992.[17] Dempster A P, Laird N M, Rubin D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society：Series B, 1977, 39(1): 1- 38.。

最优套期保值比率公式

最优套期保值比率公式【篇一：最优套期保值比率公式】套期保值率指的是为达到理想的保值效果，套期保值者在建立交易头寸时所确定的期货合约的总值与所保值的现货合同总价值之同的比率关系。

设现货市场损失为a，期货市场获利为b，则a=8.45%-8.3%，b=（1-8.45%）-（1-8.5%）=0.05%，即a=3b，实现完全套期保值即在现货市场的损失正好由期货市场抵补，所以套期保值率应为300%【篇二：最优套期保值比率公式】例如，在2002年3月份，某持有价值eur4咖万的债券组合，并决定在2个月后进行减持。

如果市场利率上扬，那么该将会有债券价格下跌的风险，为消除这一担心，该投资者决定用欧洲债券期货(每张合约包含100旧张面值eurl00的德国联邦政府债券)进行套期保值，以锁定价格，规避利率风险。

2002年3月份的市场情况见下表。

2002年3月份市场情况【篇三：最优套期保值比率公式】硕士学位论文ＭＡＳＴＥＲ’ＳＴＨＥＳＩＳ中文摘要套期保值就是在期货市场买进或卖出与现货数量相等但交易方向相反的商品期货合约，以期在未来某一时间通过卖出或买进期货合约而补偿因现货市场价格不利变动所带来的实际损失。

也就是说，套期保值是以规避现货价格风险为目的的期货交易行为，它为投资者回避、转移或者分散价格风险提供了一种有用的手段【ｌ】．最幼稚的套期保值策略是套期保值比为１．０的方法，但这种方法没有用到市场上的任何信息，所以并不理想。

传统的套期保值策略是常数套期保值方法，将现货资产收益对期货资产收益回归，由最小二乘法可求得最优套期保值比，但这种方法的不足之处是扰动项可能存在异方差，而且也没有反映出市场信息对组合资产的影响。

最近比较流行的方法是随时间变化的套期保值比，这种方法反映出了市场对组合资产的影响，而且在样本内有非常好的性质。

Ｈａｍｉｌｔｏｎ（１９８９）提出了马尔科夫状态转换模型，并且其应用在计量经济学中越来越广泛，并且ＨａｍｉｌｔｏｎａｎｄＳｕｓｍｅｌ（１９９４）提出了ＳＷＡＲＣＨ模型，认为方差随状态变化。

Controlled MCMC for Optimal Sampling

Controlled MCMC for Optimal SamplingChristophe AndrieuDepartment of Mathematics,University of Bristol,Bristol,U.K.Christian P.RobertCeremade-Universit´e Paris-Dauphine,Paris,FranceSummary.In this paper we develop an original and general framework for automatically op-timizing the statistical properties of Markov chain Monte Carlo(MCMC)samples,which are typically used to evaluate complex integrals.The Metropolis-Hastings algorithm is the basic building block of classical MCMC methods and requires the choice of a proposal distribution, which usually belongs to a parametric family.The correlation properties together with the ex-ploratory ability of the Markov chain heavily depend on the choice of the proposal distribution.By monitoring the simulated path,our approach allows us to learn“on theﬂy”the optimal pa-rameters of the proposal distribution for several statistical criteria.Monte Carlo,adaptive MCMC,calibration,stochastic approximation,gradient method, optimal scaling,random walk,Langevin,Gibbs,controlled Markov chain,learning algorithm, reversible jump MCMC.1.Motivation1.1.Introduction2 C.Andrieu and C.P.Robert1.2.Criteria for Global AdaptationControlled MCMC3 1.3.Criteria for Local Adaptation1.4.Learning Techniques4 C.Andrieu and C.P.Robert1.5.A Controlled Markov Chain ApproachControlled MCMC56 C.Andrieu and C.P.Robert2.Controlled MCMC for Adaptation 2.1.Illustrative CriteriaControlled MCMC7 2.2.The Robbins-Monro Algorithm8 C.Andrieu and C.P.Robert3.Practical and Theoretical Aspects of Stochastic Approximation Algorithms 3.1.Existence of the Gradient and Gradient-free AlgorithmsControlled MCMC9 3.2.Acceleration Techniques3.3.Stability and Convergence results10 C.Andrieu and C.P.Robert4.Coerced Acceptance Probability5.Efﬁciency Optimization of a Single MH Kernel5.2.Expression of the Gradient5.4.Main Iterationiterations(left:initial iterations/right:ﬁnal iterations ending at).and the random walk proposal.iterations(left:initial iterations/right:ﬁnal iterations ending at.distribution and the random walk proposal.(together with the smoothed estimate)and the random walk proposal.7.3.Efﬁciency Maximization:Multivariate Gaussian Random Walk7.4.Efﬁciency Maximization:Optimal Mixture of Strategies(green).sampled().proposal distributions.Gaussian target and proposal distributions.distributions.8.Discussionexample,along with steps of the corresponding Markov chain.9.AcknowledgmentsA.Gradient ofA.1.FunctionA.2.Integral Differentiationngevin Algorithm。

使用MATLAB贝叶斯工具箱（BNT），进行吉布斯采样（GibbsSampling）之前需。。。

使⽤MATLAB贝叶斯⼯具箱（BNT），进⾏吉布斯采样（GibbsSampling）之前需。

使⽤BNT(Bayesian Networks Toolbox)进⾏推断时，内置了吉布斯采样算法(即gibbs_sampling_inf_engine)，但是如果调⽤这个引擎做推断会报错。

报错内容⼤概是compute_posterior这个函数没有找到，然后进⼊..\@gibbs_sampling_inf_engine\private这个⽬录可以发现⼀个叫compute_posterior.c的⽂件，并没有.m⽂件，MATLAB当然不能调⽤C语⾔⽂件，所以需要对C⽂件进⾏编译，编译成为MATLAB可以调⽤的MEX⽂件，具体的⽅法为： 1)使MATLAB的current folder进⼊ ..\@gibbs_sampling_inf_engine\private这个⽂件夹 2)>>mex compute_posterior.c 3)>>mex sample_single_discrete.c 编译完了之后，就会⽣成compute_posterior.mexw64和sample_single_discrete.mexw64两个⽂件(后⾯的位数根据操作系统的位数不同⽽改变，本⼈电脑系统64位所以是64)。

然后需要重新导⼊matlab的search path，进⼊set path中，如果MATLAB中没有额外的⼯具箱的话，可以先使⽤Default还原为默认，然后使⽤Add with Subfolders，添加整个贝叶斯⼯具箱。

如果有其他⼯具箱的话，就不要还原了，可以remove原来的BNT⽂件夹，然后再Add with Subfolders新的⼯具箱。

添加好了就save⼀下，然后吉布斯采样的函数就可以使⽤啦~。

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