TRACTABILITY OF MULTIVARIATE INTEGRATION PROBLEM FOR PERIODIC CONTINUOUS FUNCTIONS
SPSS术语中英文对照
SPSS术语中英文对照【常用软件】SPSS术语中英文对照Absolute deviation, 绝对离差Absolute number, 绝对数Absolute residuals, 绝对残差Acceleration array, 加速度立体阵Acceleration in an arbitrary direction, 任意方向上的加速度Acceleration normal, 法向加速度Acceleration space dimension, 加速度空间的维数Acceleration tangential, 切向加速度Acceleration vector, 加速度向量Acceptable hypothesis, 可接受假设Accumulation, 累积Accuracy, 准确度Actual frequency, 实际频数Adaptive estimator, 自适应估计量Addition, 相加Addition theorem, 加法定理Additivity, 可加性Adjusted rate, 调整率Adjusted value, 校正值Admissible error, 容许误差Aggregation, 聚集性Alternative hypothesis, 备择假设Among groups, 组间Amounts, 总量Analysis of correlation, 相关分析Analysis of covariance, 协方差分析Analysis of regression, 回归分析Analysis of time series, 时间序列分析Analysis of variance, 方差分析Angular transformation, 角转换ANOVA (analysis of variance), 方差分析ANOVA Models, 方差分析模型Arcing, 弧/弧旋Arcsine transformation, 反正弦变换Area under the curve, 曲线面积AREG , 评估从一个时间点到下一个时间点回归相关时的误差ARIMA, 季节和非季节性单变量模型的极大似然估计Arithmetic grid paper, 算术格纸Arithmetic mean, 算术平均数Arrhenius relation, 艾恩尼斯关系Assessing fit, 拟合的评估Associative laws, 结合律Asymmetric distribution, 非对称分布Asymptotic bias, 渐近偏倚Asymptotic efficiency, 渐近效率Asymptotic variance, 渐近方差Attributable risk, 归因危险度Attribute data, 属性资料Attribution, 属性Autocorrelation, 自相关Autocorrelation of residuals, 残差的自相关Average, 平均数Average confidence interval length, 平均置信区间长度Average growth rate, 平均增长率Bar chart, 条形图Bar graph, 条形图Base period, 基期Bayes' theorem , Bayes定理Bell-shaped curve, 钟形曲线Bernoulli distribution, 伯努力分布Best-trim estimator, 最好切尾估计量Bias, 偏性Binary logistic regression, 二元逻辑斯蒂回归Binomial distribution, 二项分布Bisquare, 双平方Bivariate Correlate, 二变量相关Bivariate normal distribution, 双变量正态分布Bivariate normal population, 双变量正态总体Biweight interval, 双权区间Biweight M-estimator, 双权M估计量Block, 区组/配伍组BMDP(Biomedical computer programs), BMDP统计软件包Boxplots, 箱线图/箱尾图Breakdown bound, 崩溃界/崩溃点Canonical correlation, 典型相关Caption, 纵标目Case-control study, 病例对照研究Categorical variable, 分类变量Catenary, 悬链线Cauchy distribution, 柯西分布Cause-and-effect relationship, 因果关系Cell, 单元Censoring, 终检Center of symmetry, 对称中心Centering and scaling, 中心化和定标Central tendency, 集中趋势Central value, 中心值CHAID -χ2 Automatic Interac tion Detector, 卡方自动交互检测Chance, 机遇Chance error, 随机误差Chance variable, 随机变量Characteristic equation, 特征方程Characteristic root, 特征根Characteristic vector, 特征向量Chebshev criterion of fit, 拟合的切比雪夫准则Chernoff faces, 切尔诺夫脸谱图Chi-square test, 卡方检验/χ2检验Choleskey decomposition, 乔洛斯基分解Circle chart, 圆图Class interval, 组距Class mid-value, 组中值Class upper limit, 组上限Classified variable, 分类变量Cluster analysis, 聚类分析Cluster sampling, 整群抽样Code, 代码Coded data, 编码数据Coding, 编码Coefficient of contingency, 列联系数Coefficient of determination, 决定系数Coefficient of multiple correlation, 多重相关系数Coefficient of partial correlation, 偏相关系数Coefficient of production-moment correlation, 积差相关系数Coefficient of rank correlation, 等级相关系数Coefficient of regression, 回归系数Coefficient of skewness, 偏度系数Coefficient of variation, 变异系数Cohort study, 队列研究Column, 列Column effect, 列效应Column factor, 列因素Combination pool, 合并Combinative table, 组合表Common factor, 共性因子Common regression coefficient, 公共回归系数Common value, 共同值Common variance, 公共方差Common variation, 公共变异Communality variance, 共性方差Comparability, 可比性Comparison of bathes, 批比较Comparison value, 比较值Compartment model, 分部模型Compassion, 伸缩Complement of an event, 补事件Complete association, 完全正相关Complete dissociation, 完全不相关Complete statistics, 完备统计量Completely randomized design, 完全随机化设计Composite event, 联合事件Composite events, 复合事件Concavity, 凹性Conditional expectation, 条件期望Conditional likelihood, 条件似然Conditional probability, 条件概率Conditionally linear, 依条件线性Confidence interval, 置信区间Confidence limit, 置信限Confidence lower limit, 置信下限Confidence upper limit, 置信上限Confirmatory Factor Analysis , 验证性因子分析Confirmatory research, 证实性实验研究Confounding factor, 混杂因素Conjoint, 联合分析Consistency, 相合性Consistency check, 一致性检验Consistent asymptotically normal estimate, 相合渐近正态估计Consistent estimate, 相合估计Constrained nonlinear regression, 受约束非线性回归Constraint, 约束Contaminated distribution, 污染分布Contaminated Gausssian, 污染高斯分布Contaminated normal distribution, 污染正态分布Contamination, 污染Contamination model, 污染模型Contingency table, 列联表Contour, 边界线Contribution rate, 贡献率Control, 对照Controlled experiments, 对照实验Conventional depth, 常规深度Convolution, 卷积Corrected factor, 校正因子Corrected mean, 校正均值Correction coefficient, 校正系数Correctness, 正确性Correlation coefficient, 相关系数Correlation index, 相关指数Correspondence, 对应Counting, 计数Counts, 计数/频数Covariance, 协方差Covariant, 共变Cox Regression, Cox回归Criteria for fitting, 拟合准则Criteria of least squares, 最小二乘准则Critical ratio, 临界比Critical region, 拒绝域Critical value, 临界值Cross-over design, 交叉设计Cross-section analysis, 横断面分析Cross-section survey, 横断面调查Crosstabs , 交叉表Cross-tabulation table, 复合表Cube root, 立方根Cumulative distribution function, 分布函数Cumulative probability, 累计概率Curvature, 曲率/弯曲Curvature, 曲率Curve fit , 曲线拟和Curve fitting, 曲线拟合Curvilinear regression, 曲线回归Curvilinear relation, 曲线关系Cut-and-try method, 尝试法Cycle, 周期Cyclist, 周期性D test, D检验Data acquisition, 资料收集Data bank, 数据库Data capacity, 数据容量Data deficiencies, 数据缺乏Data handling, 数据处理Data manipulation, 数据处理Data processing, 数据处理Data reduction, 数据缩减Data set, 数据集Data sources, 数据来源Data transformation, 数据变换Data validity, 数据有效性Data-in, 数据输入Data-out, 数据输出Dead time, 停滞期Degree of freedom, 自由度Degree of precision, 精密度Degree of reliability, 可靠性程度Degression, 递减Density function, 密度函数Density of data points, 数据点的密度Dependent variable, 应变量/依变量/因变量Dependent variable, 因变量Depth, 深度Derivative matrix, 导数矩阵Derivative-free methods, 无导数方法Design, 设计Determinacy, 确定性Determinant, 行列式Determinant, 决定因素Deviation, 离差Deviation from average, 离均差Diagnostic plot, 诊断图Dichotomous variable, 二分变量Differential equation, 微分方程Direct standardization, 直接标准化法Discrete variable, 离散型变量DISCRIMINANT, 判断Discriminant analysis, 判别分析Discriminant coefficient, 判别系数Discriminant function, 判别值Dispersion, 散布/分散度Disproportional, 不成比例的Disproportionate sub-class numbers, 不成比例次级组含量Distribution free, 分布无关性/免分布Distribution shape, 分布形状Distribution-free method, 任意分布法Distributive laws, 分配律Disturbance, 随机扰动项Dose response curve, 剂量反应曲线Double blind method, 双盲法Double blind trial, 双盲试验Double exponential distribution, 双指数分布Double logarithmic, 双对数Downward rank, 降秩Dual-space plot, 对偶空间图DUD, 无导数方法Duncan's new multiple range method, 新复极差法/Duncan新法Effect, 实验效应Eigenvalue, 特征值Eigenvector, 特征向量Ellipse, 椭圆Empirical distribution, 经验分布Empirical probability, 经验概率单位Enumeration data, 计数资料Equal sun-class number, 相等次级组含量Equally likely, 等可能Equivariance, 同变性Error, 误差/错误Error of estimate, 估计误差Error type I, 第一类错误Error type II, 第二类错误Estimand, 被估量Estimated error mean squares, 估计误差均方Estimated error sum of squares, 估计误差平方和Euclidean distance, 欧式距离Event, 事件Event, 事件Exceptional data point, 异常数据点Expectation plane, 期望平面Expectation surface, 期望曲面Expected values, 期望值Experiment, 实验Experimental sampling, 试验抽样Experimental unit, 试验单位Explanatory variable, 说明变量Exploratory data analysis, 探索性数据分析Explore Summarize, 探索-摘要Exponential curve, 指数曲线Exponential growth, 指数式增长EXSMOOTH, 指数平滑方法Extended fit, 扩充拟合Extra parameter, 附加参数Extrapolation, 外推法Extreme observation, 末端观测值Extremes, 极端值/极值F distribution, F分布F test, F检验Factor, 因素/因子Factor analysis, 因子分析Factor Analysis, 因子分析Factor score, 因子得分Factorial, 阶乘Factorial design, 析因试验设计False negative, 假阴性False negative error, 假阴性错误Family of distributions, 分布族Family of estimators, 估计量族Fanning, 扇面Fatality rate, 病死率Field investigation, 现场调查Field survey, 现场调查Finite population, 有限总体Finite-sample, 有限样本First derivative, 一阶导数First principal component, 第一主成分First quartile, 第一四分位数Fisher information, 费雪信息量Fitted value, 拟合值Fitting a curve, 曲线拟合Fixed base, 定基Fluctuation, 随机起伏Forecast, 预测Four fold table, 四格表Fourth, 四分点Fraction blow, 左侧比率Fractional error, 相对误差Frequency, 频率Frequency polygon, 频数多边图Frontier point, 界限点Function relationship, 泛函关系Gamma distribution, 伽玛分布Gauss increment, 高斯增量Gaussian distribution, 高斯分布/正态分布Gauss-Newton increment, 高斯-牛顿增量General census, 全面普查GENLOG (Generalized liner models), 广义线性模型Geometric mean, 几何平均数Gini's mean difference, 基尼均差GLM (General liner models), 一般线性模型Goodness of fit, 拟和优度/配合度Gradient of determinant, 行列式的梯度Graeco-Latin square, 希腊拉丁方Grand mean, 总均值Gross errors, 重大错误Gross-error sensitivity, 大错敏感度Group averages, 分组平均Grouped data, 分组资料Guessed mean, 假定平均数Half-life, 半衰期Hampel M-estimators, 汉佩尔M估计量Happenstance, 偶然事件Harmonic mean, 调和均数Hazard function, 风险均数Hazard rate, 风险率Heading, 标目Heavy-tailed distribution, 重尾分布Hessian array, 海森立体阵Heterogeneity, 不同质Heterogeneity of variance, 方差不齐Hierarchical classification, 组内分组Hierarchical clustering method, 系统聚类法High-leverage point, 高杠杆率点HILOGLINEAR, 多维列联表的层次对数线性模型Hinge, 折叶点Histogram, 直方图Historical cohort study, 历史性队列研究Holes, 空洞HOMALS, 多重响应分析Homogeneity of variance, 方差齐性Homogeneity test, 齐性检验Huber M-estimators, 休伯M估计量Hyperbola, 双曲线Hypothesis testing, 假设检验Hypothetical universe, 假设总体Impossible event, 不可能事件Independence, 独立性Independent variable, 自变量Index, 指标/指数Indirect standardization, 间接标准化法Individual, 个体Inference band, 推断带Infinite population, 无限总体Infinitely great, 无穷大Infinitely small, 无穷小Influence curve, 影响曲线Information capacity, 信息容量Initial condition, 初始条件Initial estimate, 初始估计值Initial level, 最初水平Interaction, 交互作用Interaction terms, 交互作用项Intercept, 截距Interpolation, 内插法Interquartile range, 四分位距Interval estimation, 区间估计Intervals of equal probability, 等概率区间Intrinsic curvature, 固有曲率Invariance, 不变性Inverse matrix, 逆矩阵Inverse probability, 逆概率Inverse sine transformation, 反正弦变换Iteration, 迭代Jacobian determinant, 雅可比行列式Joint distribution function, 分布函数Joint probability, 联合概率Joint probability distribution, 联合概率分布K means method, 逐步聚类法Kaplan-Meier, 评估事件的时间长度Kaplan-Merier chart, Kaplan-Merier图Kendall's rank correlation, Kendall等级相关Kinetic, 动力学Kolmogorov-Smirnove test, 柯尔莫哥洛夫-斯米尔诺夫检验Kruskal and Wallis test, Kruskal及Wallis检验/多样本的秩和检验/H检验Kurtosis, 峰度Lack of fit, 失拟Ladder of powers, 幂阶梯Lag, 滞后Large sample, 大样本Large sample test, 大样本检验Latin square, 拉丁方Latin square design, 拉丁方设计Leakage, 泄漏Least favorable configuration, 最不利构形Least favorable distribution, 最不利分布Least significant difference, 最小显著差法Least square method, 最小二乘法Least-absolute-residuals estimates, 最小绝对残差估计Least-absolute-residuals fit, 最小绝对残差拟合Least-absolute-residuals line, 最小绝对残差线Legend, 图例L-estimator, L估计量L-estimator of location, 位置L估计量L-estimator of scale, 尺度L估计量Level, 水平Life expectance, 预期期望寿命Life table, 寿命表Life table method, 生命表法Light-tailed distribution, 轻尾分布Likelihood function, 似然函数Likelihood ratio, 似然比line graph, 线图Linear correlation, 直线相关Linear equation, 线性方程Linear programming, 线性规划Linear regression, 直线回归Linear Regression, 线性回归Linear trend, 线性趋势Loading, 载荷Location and scale equivariance, 位置尺度同变性Location equivariance, 位置同变性Location invariance, 位置不变性Location scale family, 位置尺度族Log rank test, 时序检验Logarithmic curve, 对数曲线Logarithmic normal distribution, 对数正态分布Logarithmic scale, 对数尺度Logarithmic transformation, 对数变换Logic check, 逻辑检查Logistic distribution, 逻辑斯特分布Logit transformation, Logit转换LOGLINEAR, 多维列联表通用模型Lognormal distribution, 对数正态分布Lost function, 损失函数Low correlation, 低度相关Lower limit, 下限Lowest-attained variance, 最小可达方差LSD, 最小显著差法的简称Lurking variable, 潜在变量Main effect, 主效应Major heading, 主辞标目Marginal density function, 边缘密度函数Marginal probability, 边缘概率Marginal probability distribution, 边缘概率分布Matched data, 配对资料Matched distribution, 匹配过分布Matching of distribution, 分布的匹配Matching of transformation, 变换的匹配Mathematical expectation, 数学期望Mathematical model, 数学模型Maximum L-estimator, 极大极小L 估计量Maximum likelihood method, 最大似然法Mean, 均数Mean squares between groups, 组间均方Mean squares within group, 组内均方Means (Compare means), 均值-均值比较Median, 中位数Median effective dose, 半数效量Median lethal dose, 半数致死量Median polish, 中位数平滑Median test, 中位数检验Minimal sufficient statistic, 最小充分统计量Minimum distance estimation, 最小距离估计Minimum effective dose, 最小有效量Minimum lethal dose, 最小致死量Minimum variance estimator, 最小方差估计量MINITAB, 统计软件包Minor heading, 宾词标目Missing data, 缺失值Model specification, 模型的确定Modeling Statistics , 模型统计Models for outliers, 离群值模型Modifying the model, 模型的修正Modulus of continuity, 连续性模Morbidity, 发病率Most favorable configuration, 最有利构形Multidimensional Scaling (ASCAL), 多维尺度/多维标度Multinomial Logistic Regression , 多项逻辑斯蒂回归Multiple comparison, 多重比较Multiple correlation , 复相关Multiple covariance, 多元协方差Multiple linear regression, 多元线性回归Multiple response , 多重选项Multiple solutions, 多解Multiplication theorem, 乘法定理Multiresponse, 多元响应Multi-stage sampling, 多阶段抽样Multivariate T distribution, 多元T分布Mutual exclusive, 互不相容Mutual independence, 互相独立Natural boundary, 自然边界Natural dead, 自然死亡Natural zero, 自然零Negative correlation, 负相关Negative linear correlation, 负线性相关Negatively skewed, 负偏Newman-Keuls method, q检验NK method, q检验No statistical significance, 无统计意义Nominal variable, 名义变量Nonconstancy of variability, 变异的非定常性Nonlinear regression, 非线性相关Nonparametric statistics, 非参数统计Nonparametric test, 非参数检验Nonparametric tests, 非参数检验Normal deviate, 正态离差Normal distribution, 正态分布Normal equation, 正规方程组Normal ranges, 正常范围Normal value, 正常值Nuisance parameter, 多余参数/讨厌参数Null hypothesis, 无效假设Numerical variable, 数值变量Objective function, 目标函数Observation unit, 观察单位Observed value, 观察值One sided test, 单侧检验One-way analysis of variance, 单因素方差分析Oneway ANOVA , 单因素方差分析Open sequential trial, 开放型序贯设计Optrim, 优切尾Optrim efficiency, 优切尾效率Order statistics, 顺序统计量Ordered categories, 有序分类Ordinal logistic regression , 序数逻辑斯蒂回归Ordinal variable, 有序变量Orthogonal basis, 正交基Orthogonal design, 正交试验设计Orthogonality conditions, 正交条件ORTHOPLAN, 正交设计Outlier cutoffs, 离群值截断点Outliers, 极端值OVERALS , 多组变量的非线性正规相关Overshoot, 迭代过度Paired design, 配对设计Paired sample, 配对样本Pairwise slopes, 成对斜率Parabola, 抛物线Parallel tests, 平行试验Parameter, 参数Parametric statistics, 参数统计Parametric test, 参数检验Partial correlation, 偏相关Partial regression, 偏回归Partial sorting, 偏排序Partials residuals, 偏残差Pattern, 模式Pearson curves, 皮尔逊曲线Peeling, 退层Percent bar graph, 百分条形图Percentage, 百分比Percentile, 百分位数Percentile curves, 百分位曲线Periodicity, 周期性Permutation, 排列P-estimator, P估计量Pie graph, 饼图Pitman estimator, 皮特曼估计量Pivot, 枢轴量Planar, 平坦Planar assumption, 平面的假设PLANCARDS, 生成试验的计划卡Point estimation, 点估计Poisson distribution, 泊松分布Polishing, 平滑Polled standard deviation, 合并标准差Polled variance, 合并方差Polygon, 多边图Polynomial, 多项式Polynomial curve, 多项式曲线Population, 总体Population attributable risk, 人群归因危险度Positive correlation, 正相关Positively skewed, 正偏Posterior distribution, 后验分布Power of a test, 检验效能Precision, 精密度Predicted value, 预测值Preliminary analysis, 预备性分析Principal component analysis, 主成分分析Prior distribution, 先验分布Prior probability, 先验概率Probabilistic model, 概率模型probability, 概率Probability density, 概率密度Product moment, 乘积矩/协方差Profile trace, 截面迹图Proportion, 比/构成比Proportion allocation in stratified random sampling, 按比例分层随机抽样Proportionate, 成比例Proportionate sub-class numbers, 成比例次级组含量Prospective study, 前瞻性调查Proximities, 亲近性Pseudo F test, 近似F检验Pseudo model, 近似模型Pseudosigma, 伪标准差Purposive sampling, 有目的抽样QR decomposition, QR分解Quadratic approximation, 二次近似Qualitative classification, 属性分类Qualitative method, 定性方法Quantile-quantile plot, 分位数-分位数图/Q-Q图Quantitative analysis, 定量分析Quartile, 四分位数Quick Cluster, 快速聚类Radix sort, 基数排序Random allocation, 随机化分组Random blocks design, 随机区组设计Random event, 随机事件Randomization, 随机化Range, 极差/全距Rank correlation, 等级相关Rank sum test, 秩和检验Rank test, 秩检验Ranked data, 等级资料Rate, 比率Ratio, 比例Raw data, 原始资料Raw residual, 原始残差Rayleigh's test, 雷氏检验Rayleigh's Z, 雷氏Z值Reciprocal, 倒数Reciprocal transformation, 倒数变换Recording, 记录Redescending estimators, 回降估计量Reducing dimensions, 降维Re-expression, 重新表达Reference set, 标准组Region of acceptance, 接受域Regression coefficient, 回归系数Regression sum of square, 回归平方和Rejection point, 拒绝点Relative dispersion, 相对离散度Relative number, 相对数Reliability, 可靠性Reparametrization, 重新设置参数Replication, 重复Report Summaries, 报告摘要Residual sum of square, 剩余平方和Resistance, 耐抗性Resistant line, 耐抗线Resistant technique, 耐抗技术R-estimator of location, 位置R估计量R-estimator of scale, 尺度R估计量Retrospective study, 回顾性调查Ridge trace, 岭迹Ridit analysis, Ridit分析Rotation, 旋转Rounding, 舍入Row, 行Row effects, 行效应Row factor, 行因素RXC table, RXC表Sample, 样本Sample regression coefficient, 样本回归系数Sample size, 样本量Sample standard deviation, 样本标准差Sampling error, 抽样误差SAS(Statistical analysis system ), SAS统计软件包Scale, 尺度/量表Scatter diagram, 散点图Schematic plot, 示意图/简图Score test, 计分检验Screening, 筛检SEASON, 季节分析Second derivative, 二阶导数Second principal component, 第二主成分SEM (Structural equation modeling), 结构化方程模型Semi-logarithmic graph, 半对数图Semi-logarithmic paper, 半对数格纸Sensitivity curve, 敏感度曲线Sequential analysis, 贯序分析Sequential data set, 顺序数据集Sequential design, 贯序设计Sequential method, 贯序法Sequential test, 贯序检验法Serial tests, 系列试验Short-cut method, 简捷法Sigmoid curve, S形曲线Sign function, 正负号函数Sign test, 符号检验Signed rank, 符号秩Significance test, 显著性检验Significant figure, 有效数字Simple cluster sampling, 简单整群抽样Simple correlation, 简单相关Simple random sampling, 简单随机抽样Simple regression, 简单回归simple table, 简单表Sine estimator, 正弦估计量Single-valued estimate, 单值估计Singular matrix, 奇异矩阵Skewed distribution, 偏斜分布Skewness, 偏度Slash distribution, 斜线分布Slope, 斜率Smirnov test, 斯米尔诺夫检验Source of variation, 变异来源Spearman rank correlation, 斯皮尔曼等级相关Specific factor, 特殊因子Specific factor variance, 特殊因子方差Spectra , 频谱Spherical distribution, 球型正态分布Spread, 展布SPSS(Statistical package for the social science), SPSS统计软件包Spurious correlation, 假性相关Square root transformation, 平方根变换Stabilizing variance, 稳定方差Standard deviation, 标准差Standard error, 标准误Standard error of difference, 差别的标准误Standard error of estimate, 标准估计误差Standard error of rate, 率的标准误Standard normal distribution, 标准正态分布Standardization, 标准化Starting value, 起始值Statistic, 统计量Statistical control, 统计控制Statistical graph, 统计图Statistical inference, 统计推断Statistical table, 统计表Steepest descent, 最速下降法Stem and leaf display, 茎叶图Step factor, 步长因子Stepwise regression, 逐步回归Storage, 存Strata, 层(复数)Stratified sampling, 分层抽样Stratified sampling, 分层抽样Strength, 强度Stringency, 严密性Structural relationship, 结构关系Studentized residual, 学生化残差/t化残差Sub-class numbers, 次级组含量Subdividing, 分割Sufficient statistic, 充分统计量Sum of products, 积和Sum of squares, 离差平方和Sum of squares about regression, 回归平方和Sum of squares between groups, 组间平方和Sum of squares of partial regression, 偏回归平方和Sure event, 必然事件Survey, 调查Survival, 生存分析Survival rate, 生存率Suspended root gram, 悬吊根图Symmetry, 对称Systematic error, 系统误差Systematic sampling, 系统抽样Tags, 标签Tail area, 尾部面积Tail length, 尾长Tail weight, 尾重Tangent line, 切线Target distribution, 目标分布Taylor series, 泰勒级数Tendency of dispersion, 离散趋势Testing of hypotheses, 假设检验Theoretical frequency, 理论频数Time series, 时间序列Tolerance interval, 容忍区间Tolerance lower limit, 容忍下限Tolerance upper limit, 容忍上限Torsion, 扰率Total sum of square, 总平方和Total variation, 总变异Transformation, 转换Treatment, 处理Trend, 趋势Trend of percentage, 百分比趋势Trial, 试验Trial and error method, 试错法Tuning constant, 细调常数Two sided test, 双向检验Two-stage least squares, 二阶最小平方Two-stage sampling, 二阶段抽样Two-tailed test, 双侧检验Two-way analysis of variance, 双因素方差分析Two-way table, 双向表Type I error, 一类错误/α错误Type II error, 二类错误/β错误UMVU, 方差一致最小无偏估计简称Unbiased estimate, 无偏估计Unconstrained nonlinear regression , 无约束非线性回归Unequal subclass number, 不等次级组含量Ungrouped data, 不分组资料Uniform coordinate, 均匀坐标Uniform distribution, 均匀分布Uniformly minimum variance unbiased estimate, 方差一致最小无偏估计Unit, 单元Unordered categories, 无序分类Upper limit, 上限Upward rank, 升秩Vague concept, 模糊概念Validity, 有效性VARCOMP (Variance component estimation), 方差元素估计Variability, 变异性Variable, 变量Variance, 方差Variation, 变异Varimax orthogonal rotation, 方差最大正交旋转Volume of distribution, 容积W test, W检验Weibull distribution, 威布尔分布Weight, 权数Weighted Chi-square test, 加权卡方检验/Cochran检验Weighted linear regression method, 加权直线回归Weighted mean, 加权平均数Weighted mean square, 加权平均方差Weighted sum of square, 加权平方和Weighting coefficient, 权重系数Weighting method, 加权法W-estimation, W估计量W-estimation of location, 位置W估计量Width, 宽度Wilcoxon paired test, 威斯康星配对法/配对符号秩和检验Wild point, 野点/狂点Wild value, 野值/狂值Winsorized mean, 缩尾均值Withdraw, 失访Youden's index, 尤登指数Z test, Z检验Zero correlation, 零相关Z-transformation, Z变换。
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SPSS统计词汇
英汉统计学常用词汇(SPSS)Aabsolute deviation 绝对离差absolute residuals 绝对残差acceptable hypothesis 可接受假设acceptable region 接受域actual frequency 实际频数adaptive estimator 自适应估计量addition theorem 加法定理additivity 可加性adjusted R square 调整判别系数admissible error 容许误差alphafactorin g α因子提取法alternative hypothesis 备择假设among groups 组间analysis of correlation 相关分析analysis of covariance 协方差分析analysis of regression 回归分析BBayesian estimation Beyes估计bell-shaped curve 钟形曲线best-trim estimator 最好切尾估计量beta distribution β分布between groups 组间的between measures 重复测量间的bivariate 双变量的bivariate correlate 二变量相关biweight interval 双权区间biweight M-estimator 双权M估计量block 区组/配伍组boxplot 箱线图Ccanonical correlation 典型相关case-control study 病例对照研究categorical variable 分类变量Cauchy distribution 柯西分布centering and scaling 中心化和定标central tendency 集中趋势chance statistics 随机统计量chance variable 随机变量chi-square distribution 卡方分布chi-square statistics 卡方统计量chi-square test 卡方检验classified variable 分类变量coefficient of skewness 偏度系数coefficient of variation 变异系数communality variance 共性方差compare means 均值比较分析complete association 完全正相关concomitant variable 伴随变量conditional likelihood 条件似然conditional probability 条件概率confidence limit 置信限consistency check 一致性检验consistent estimate 一致估计contingency tables 列联表continuous variable 连续变量control charts 控制图controlled experiments 对照实验conventional depth 常规深度correction coefficient 校正系数critical point 临界点critical ratio 临界比cumulative probability 累计概率curvature 曲率cyclist 周期性Ddata capacity 数据容量data deficiencies 数据缺乏1data handling 数据处理data reduction 数据简化分析data transformation 数据变换degree of precision 精密度degree of reliability 可靠性程度density function 密度函数density of data points 数据点的密度derivative matrix 导数矩阵description 描述descriptive 描述性的deviation from average 离均差Df. Fit 拟合差值df(degree of freedom)自由度dichotomous variable 二分变量discriminant analysis 判别分析discriminant coefficient 判别系数disproportional 不成比例的dissimilarity 不相似性distribution shape 分布形状disturbance 随机扰动项double logarithmic 双对数Eeffect 实验效应effects of interaction 交互效应efficiency 有效性eigenvector 特征向量enumeration data 计数资料equal size 相等的数量error of estimate 估计误差error type Ⅰ第一类错误error type Ⅱ第二类错误estimation 估计量Euclidean distance 欧氏距离expectation plane 期望平面expectation surface 期望曲面expected value 期望值experimental sampling 试验抽样explanatory variable 解释变量explore Summarize 探索-摘要EXSMOOTH 指数平滑方法extended fit 扩充拟合extra parameter 附加参数extreme observation 末端观测值extreme value 极值Ffactor score 因子得分factorial designs 因子设计factorial experiment 因子试验failure rate 失效率family of estimators 估计量族fatality rate 病死率finite population 有限总体finite-sample 有限样本first derivative 一阶导数first quartile 第一四分位数Fisher information Fisher信息量fitting a curve 曲线拟合fixed model 固定模型fixed variable 固定变量fluctuation 随机起伏fourth 四分点fractional error 相对误差frequency polygon 频数多边图frontier point 界限点F-test F检验function 函数function relationship 泛函关系Ggamma distribution 伽玛分布general census 全面普查geometric mean几何均值Gini’s mean difference 基尼均差goodness-of-fit 拟合优度gross-error sensitivity 大错敏感度group averages 分组平均grouped data 分组资料grouped median 组中值growth curve 生长曲线Hhalf-life 半衰期happenstance 偶然事件harmonic mean 调和均值hazard function 风险均数hazard rate 风险率Hessian array Hessian立体阵Heterogeneity 不同质heterogeneity 不齐性HOMAIS 多重响应分析homogeneity of variance 方差齐性homogeneity test 齐性检验Huber M-estimators Huber M 估计量hyperbola 双曲线hypothesis 假设hypothesis test 假设检验hypothetical universe 假设总体Iimage factoring 典型因子提取法impossible event 不可能事件independent samples 独立样本independent variable 自变量indirect standardization 间接标准化法infinitely great 无穷大information capacity 信息容量interclass correlation 组内相关inter-item correlation 样本内相关interpolation 内插法interquartile range 四分位距interclass correlation 组间相关inverse matrix 逆矩阵item means 样本均值L large sample problem 大样本问题Latin square 拉丁方Latin square design 拉丁方设计Least-square estimation 最小二乘估计L-estimator of location 位置L估计量level of significance 显著性水平leverage value 中心化杠杆值life expectance 预期期望寿命life table 寿命表life table method 生命表法light-tailed distribution 轻尾分布likelihood function 似然函数likelihood ratio 似然比likelihood ratio test 似然比检验linear relation 线性关系linear trend 线性预测值loading 载荷location invariance 位置不变性log rank test 时序检验logarithmic scale 对数尺度logic check 逻辑检查logistic 逻辑的logistic distribution logistic分布logit model logit模型logit transformation logit转换logarithms 对数lost function 损失函数lower limit 下限MMahal Distance 马氏距离main effect 主效应maintainability 可维护度matched data 配对资料matched distribution 匹配分布matrix 矩阵maximum 最大值mean 均值mean difference 均值差值mean square 均方mean sum of square 均方和measure 度量median 中位数median lethal dose 半数致死量median polish 中位数平滑m-estimator M估计midpoint 中值model specification 模型的确定modeling statistics 型统计models for outliers 离群值模型modifying the model 模型的修正Monte Carle method 蒙特卡洛法multiple comparison 多重比较multiple correlation 多元相关系数multiple response 多重响应multiple response sets 多重响应集合multiple solutions 多解multiplication theorem 乘法定理multi-response 多元响应multi-stage sampling 多阶段抽样multivariate 多元的multivariate analysis 多元分析mutual exclusive 互不相容mutual independence 互相独立Nnegative correlation 负相关nominal variable 名义变量nonlinear regression 非线性相关nonlinear regression 非线性回归nonparamtric statistics 非参数统计nonparametric test 非参数检验normal distribution 正态分布normal P-P 正态P-P图normal probability 正态概率normal Q-Q 正态Q-Q图normal value 正常值null hypothesis 零假设Oobjective function 目标函数observation unit 观察单位observed value 观察值one sided test 单侧检验one-sample 单样本one-tailed test 单侧检验one-way classification 单因素分类order statistics 顺序统计量ordered categories 有序分类ordinal 序数ordinal variable 有序变量origin 原点orthogonal 正交的orthogonal design 正交试验设计Ppaired observations 成对观测数据paired design 配对设计paired sample 配对样本parametric statistics 参数统计Pearson curves Pearson曲线P-estimator P估计量pie chart 饼(圆)图Pitman estimator Pitman估计量pivot 枢轴量pivot table 枢轴表polynomial regression 多项式回归population 总体positive correlation 正相关posterior distribution 后验分布preliminary analysis 预备性分析probability 概率probability density 概率密度probability of F F显著性概率probit analysis 概率分析product moment 乘积矩/协方差QQ-Q Plot Q-Q概率图quadratic regression 二次多项式回归quadratic term 二次项quality control charts 质量控制图quantitative analysis 定量分析quartile 四分位数RR square 判别系数random 随机random event 随机事件random number 随机数random sampling 随机取样random variable 随机变量randomization 随机化rank statistic 秩统计量rank sum test 秩和检验rank test 秩检验ranked data 等级资料ratio analysis 比率分析raw data 原始资料Rayleigh's test 雷氏检验reciprocal 倒数reject region 拒绝域rejection point 拒绝点relative dispersion 相对离散度relative number 相对数reliability 可靠性reliability analysis 可靠性分析reliability test 可靠性检验report summaries 报告摘要residual 残差residual sum of square 残差平方和response 响应root mean square 均方根rotation 旋转row effects 行效应run test 游程检验S S. E.mean 均值的标准差S.E.of Kurtosis 峰度的标准差S.E.of Skewness 偏度的标准差sample size 样本容量sample space 样本空间sampling design 抽样设计sampling distribution 抽样分布sampling error 抽样误差sampling inspection 抽样检验scatter diagram 散点图schematic plot 示意图/简图score statistic 得分统计score test 计分检验sensitivity curve 敏感度曲线sequential analysis 贯序分析sequential data set 顺序数据集serial tests 系列试验series mean 系列均值sign test 符号检验signed rank 符号秩significance digits 有效数字significance test 显著性检验significant figure 有效数字similarity 相似性simple regression 简单回归skewed distribution 偏态分布skewness 偏度small sample problem 小样本问题Smirnov test Smirnov检验specific factor 特殊因子specific factor variance 特殊因子方差standard deviation 标准差standard error 标准误standard residual plots 标准化残差图standardize 标准化standardized coefficients标准化系数standardized residual 标准化残差statistics 统计学(量)、统计图表std.predicted value 标准预测值Std.residual 标准残差stem and leaf display 茎叶图step factor 步长因子stochastic models 随机模型stochastic process 随机过程survival 生存分析symmetry 对称systematic error 系统误差systematic sampling 系统抽样Ttest criterion 检验判据test for linearity 线性检验test of goodness of fit 拟和优度检验test of homogeneity 齐性检验test oh independence 独立性检验test rules 检验法则testing function 检验函数testing of hypotheses 假设检验theoretical frequency 理论频数time series 时间序列tolerance 容忍度tolerance interval 容忍区间tolerance limits 容限tolerance lower limit 容忍下限tolerance upper limit 容忍上限total sum of square 总平方和total variation 总变异transfer function 转换函数Uunbiased estimation 无偏估计unbiasedness 无偏性unequal size 不等含量unweight 不加权upper limit 上限upward rank 升秩Vvalidity 有效性value 数值value of estimator 计值variability 变异性variable 变量variance components 方差成分variance ratio 方差比variation 变异various 不同的vector 向量WWeibull distribution 威布尔分布weighted mean square 加权平均方差weighted sum of square 加权平方和weighting coefficient 权重系数weighting method 加权法weighted average 加权平均值ZZ score Z分数Z test Z检验zero correlation 零相关Z-transformation Z变换6。
计量经济学中英文词汇对照
cross-loading Cross-over design Cross-section analysis Cross-section survey
Cross-sectional
Crosstabs Cross-tabulation table Cube root Cumulative distribution function Cumulative probability Curvature Curvature Curve fit Curve fitting Curvilinear regression Curvilinear relation Cut-and-try method Cycle
Controlled experiments Conventional depth Convolution Corrected factor Corrected mean Correction coefficient Correctness Correlation coefficient Correlation index Correspondence Counting Counts Covariance Covariant Cox Regression Criteria for fitting Criteria of least squares Critical ratio Critical region Critical value
Cyclist DDD D test Data acquisition Data bank Data capacity Data deficiencies Data handling Data manipulation Data processing Data reduction Data set Data sources Data transformation Data validity Data-in Data-out Dead time Degree of freedom Degree of precision Degree of reliability Degression Density function Density of data points Dependent variable Dependent variable Depth Derivative matrix Derivative-free methods Design Determinacy Determinant Determinant Deviation Deviation from average Diagnostic plot Dichotomous variable Differential equation Direct standardization Discrete variable DISCRIMINANT Discriminant analysis Discriminant coefficient
高一生物必修2《基因工程及其应用》
一种限制酶只能识别一种特定的核苷酸序列, 并在特定的切点上切割DNA分子。 限制 例如: 限制 大肠杆菌的 酶 酶 一种限制酶
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其作用与限制性内切酶 相反,作用点相同
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生长快、肉质好的转基因 鱼(中国)
转黄瓜抗青枯病基因的甜椒
转鱼抗寒基因 的番茄
水母
斑马鱼
荧光鼠
世界上第一只基 因改造的灵长类 动物猴子的研究, 将有助於发现诸 如老年痴呆症、 爱滋病及癌症的 新基因疗法
21世纪将是基因工程迅速发展并完善
的世纪,也是它将产生巨大效益的世纪!
基因工程:又叫做基因拼接技术或DNA 重组技术。通俗的说,就是按照人们的 意愿,把一种生物的某种基因提取出来, 加以修饰改造,然后放到另一种生物的 细胞里,定向地改造生物的遗传性状。
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基因工程:又叫做基因拼接技术或DNA重组 技术。通俗的说,就是按照人们的意愿,把 一种生物的某种基因提取出来,加以修饰改 造,然后放到另一种生物的细胞里,定向地 改造生物的遗传性状。
基因工程的原理 基因工程的工具 基因工程的过程 基因工程的应用
小
结
转基因食品的安全性
反馈达标
练习
1.以下说法正确的是 (C )
A、所有的限制酶只能识别一种特定的核苷 酸序列 B、质粒是基因工程中唯一的运载体 C、运载体必须具备的条件之一是:具有多 个限制酶切点,以便与外源基因连接 D、DNA连接酶使黏性末段的碱基之间形 成氢键
1、医药卫生: a、药品生产 生 产 胰 岛 素 我国生产的部分基因
fluent操作界面中英
fluent 操作界面中英文对照Read 读取文件:scheme 方案journal 日志profile 外形 Write 保存文件Import :进入另一个运算程序 Interpolate :窜改,插入 Hardcopy : 复制, Batch options 一组选项 Save layout 保存设计Grid 网格Check 检查Info 报告:size 尺寸 ;memory usage 内存使用 情况;zones 区域;partitions 划分存储区 Polyhedral 多面体:Convert domain 变换范围Convert skewed cells 变换倾斜的单元Merge 合并 Separate 分割Fuse (Merge 的意思是将具有相同条件的边界合 并成一个;Fuse 将两个网格完全贴合的边界融合 成内部(interior)来处理,比如叶轮机中,计算多 个叶片时,只需生成一个叶片通道网格,其他通 过复制后,将重合的周期边界Fuse 掉就行了。
注意两个命令均为不可逆操作,在进行操作时注 意保存case) Zone 区域: append case file 添力口 case 文档 Replace 取代;delete 删除;deactivate 使复 位; Surface mesh 表面网孔Reordr 追加,添加:Domain 范围;zones 区域; Print bandwidth 打印 Scale 单位变换 Translate 转化Rotate 旋转 smooth/swap 光滑/交换CheckInfo ► Polyhedra►Merge...Separate ► Fuse...Zone►Surface Mesh... Reorder►Scale...Translate...Rotate...Smooth/Swap...ieGrid ] Define Solvea:w 1E3 SolverSolver* Pressure Based 'Density Based Space「2DL Axisymmetric 广 Axcsymmetric Swirl m 3DVelgty Formulatiqn • Absolute RelativeGradient Option区 Implicit「Explicit Time# SteadyUnsteadyPorous Formulation• Superficial VelocityPhysical Veiccity6K | Cancel] Help |Pressure based 基于压力 Density based 基于密度Models 模型:solver 解算器Formulation # Green-Gauss Cell Oased Green-Gauss N 。
六西格玛专业术语一览
Optimization:从进程中找出最希望的结果下,其因子和水准的组合。
Pareto Chart :以一样公制单位(次数、元额、时刻等)表示事件的条状图。
Plackett-Burman Design:一种设计的实验,用来挑选样本需要的最小量。通常只调查要紧的阻碍,而不预测彼其间的阻碍。
Gage Repeatability:当操纵者利用相同的gage衡量此明显的特性时,可取得相同的变异。
Gage Reproducibility:当衡量相同部份的特性时,由不同的操作者以相同的gage衡量其平均变异。
Generator:一个用来制造部份因子设计的彼此阻碍作用。
GLM(General Linear Model) :一个ANOVA的形式,可许诺实验设计中些许程度的不平稳。
Degrees of Freedom for Error:一个数值,用来分析变异数以预测进程中的干扰度。未对进程的干扰度加以预测,而决定何者是重要的变量及其阻碍程度,都是无效的。一个大约的衡量准那么是,5的误差的自由度为极小值,相当于至少六次的重复。
DOA(Dead on Arrival):客户接收时无法运作的产品。
R-Square:判定系数。在反映中变异百分比由操纵的因素来讲明。
Run :一套进程条件由规定实验方面所有因素的层次概念。一样,叫作处置结合。
Run chart:经营图表。提供一些统计分析能力和机率资料的持续时刻序列图。
Factor:在实验中能改变的投入要素,因子。可能以质(例如:附加的种类)或量(例如:温度、气压)表示。
Factor, Fixed:
若是要素的水准明确的被指定,那么此要素称做固定的。结论只能以此要素来推论。结果具重要性。
六西格玛术语中英文对照
六西格玛术语中英文对照$k-—Thousands of dollars千美元$M——Millions of dollars百万美元% R & R-—Gage % Repeatability and Reproducibility %重复性和再现性ANOVA——Analysis Of Variance 方差分析AOP——Annual Operating Plan年度运营计划BB——Black Belt黑带A process improvement project team leader who is trained and certified in the Six Sigma breakthrough methodology and tools, and who is responsible for project execution.经“六西格玛"方法论和工具使用培训并认证的过程改进项目的项目负责人,负责项目的执行. BOD——Board of Directors董事会BPM——Business Process Management商业流程管理BTS——Breakthrough Technology Solution 突破性改进解决方案C & E——Cause and Effects matrix因果矩阵CAP——Change Acceleration Process加速变革流程Capability——能力The total range of inherent variation in a stable process。
It is determined by using control charts data。
在稳定过程中全部内在固有变化的改变范围。
它由控制图的数据来确定。
CapabilityIndex—-能力指数A calculated value used to compare process variation to a specification。
数据挖掘中的名词解释
第一章1,数据挖掘(Data Mining),就是从存放在数据库,数据仓库或其他信息库中的大量的数据中获取有效的、新颖的、潜在有用的、最终可理解的模式的非平凡过程。
2,人工智能(Artific ial Intelli gence)它是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及应用系统的一门新的技术科学。
人工智能是计算机科学的一个分支,它企图了解智能的实质,并生产出一种新的能以人类智能相似的方式做出反应的智能机器。
3,机器学习(Machine Learnin g)是研究计算机怎样模拟或实现人类的学习行为,以获取新的知识或技能,重新组织已有的知识结构使之不断改善自身的性能。
4,知识工程(Knowled ge Enginee ring)是人工智能的原理和方法,对那些需要专家知识才能解决的应用难题提供求解的手段。
5,信息检索(Informa tion Retriev al)是指信息按一定的方式组织起来,并根据信息用户的需要找出有关的信息的过程和技术。
6,数据可视化(Data Visuali zation)是关于数据之视觉表现形式的研究;其中,这种数据的视觉表现形式被定义为一种以某种概要形式抽提出来的信息,包括相应信息单位的各种属性和变量。
7,联机事务处理系统(OLTP)实时地采集处理与事务相连的数据以及共享数据库和其它文件的地位的变化。
在联机事务处理中,事务是被立即执行的,这与批处理相反,一批事务被存储一段时间,然后再被执行。
8, 联机分析处理(OLAP)使分析人员,管理人员或执行人员能够从多角度对信息进行快速一致,交互地存取,从而获得对数据的更深入了解的一类软件技术。
8,决策支持系统(decisio n support)是辅助决策者通过数据、模型和知识,以人机交互方式进行半结构化或非结构化决策的计算机应用系统。
六西格玛术语缩写中英对照
What is 城市轨道交通 urban rail transport
精品ppt模板
公共方差 公共变异 共性方差 可比性 批比较 比较值 分部模型 伸缩 补事件 完全正相关 完全不相关 完备统计量
13
• Completely randomized design • Composite event • Composite events • Concavity • Conditional expectation • Conditional likelihood • Conditional probability • Conditionally linear • Confidence interval • Confidence limit • Confidence lower limit • Confidence upper limit
条形图
• Bar graph
条形图
ቤተ መጻሕፍቲ ባይዱ
• Base period
基期
• Bayes‘ theorem
Bayes 定理
• Bell-shaped curve
钟形曲线
What is 城市轨道交通 urban rail transport
精品ppt模板
6
• Bernoulli distribution • Best-trim estimator • Between-group variation • Bias • Binary logistic regression • Binomial distribution • Binomial tests • Bisquare • Bivariate Correlate • Bivariate normal distribution • Bivariate normal population • Biweight interval
distributed coordination of multi-agent systems with quantizaed-observer based encoding-decoding
Distributed Coordination of Multi-Agent Systems With Quantized-Observer Based Encoding-DecodingTao Li,Member,IEEE,and Lihua Xie,Fellow,IEEEAbstract—Integrative design of communication mechanism and coordinated control law is an interesting and important problem for multi-agent networks.In this paper,we consider distributed coordination of discrete-time second-order multi-agent systems with partially measurable state and a limited communication data rate.A quantized-observer based encoding-decoding scheme is designed,which integrates the state observation with encoding/de-coding.A distributed coordinated control law is proposed for each agent which is given in terms of the states of its encoder and decoders.It is shown that for a connected network,2-bit quantizers suffice for the exponential asymptotic synchronization of the states of the agents.The selection of controller parameters and the performance limit are discussed.It is shown that the alge-braic connectivity and the spectral radius of the Laplacian matrix of the communication graph play key roles in the closed-loop performance.The spectral radius of the Laplacian matrix is related to the selection of control gains,while the algebraic con-nectivity is related to the spectral radius of the closed-loop state matrix.Furthermore,it is shown that as the number of agents increases,the asymptotic convergence rate can be approximated as a function of the number of agents,the number of quantization levels(communication data rate)and the ratio of the algebraic connectivity to the spectral radius of the Laplacian matrix of the communication graph.Index Terms—Data rate,digital communication,distributed co-ordination,encoding and decoding,multi-agent systems,quantized observer.I.I NTRODUCTIONI N recent years,distributed cooperative control of multi-agent systems has attracted unprecedented attention of the control community([1]–[14])in view of its wide applications in many emergingfields such as smart grids,intelligent trans-portation,formationflight,etc.In particular,the problem of multi-agent consensus has been the focus of many researches; see,e.g.,[5]and the reference therein.Manuscript received March01,2011;revised September03,2011;accepted April05,2012.Date of publication May14,2012;date of current version November21,2012.Recommended by Associate Editor L.Schenato.This work was supported by the National Natural Science Foundation of China (NSFC)under grants61004029,60934006and61120106011.This paper was presented in part at the30th Chinese Control Conference,July22-24,2011, Yantai,China.Recommended by Associate Editor L.Schenato.T.Li is with the Key Laboratory of Systems and Control,Institute of Systems Science,Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing100190,China(e-mail:litao@).L.Xie is with EXQUISITUS,Centre for E-City,School of Electrical and Electronic Engineering,Nanyang Technological University,Singapore639798 (e-mail:elhxie@.sg).Color versions of one or more of thefigures in this paper are available online at .Digital Object Identifier10.1109/TAC.2012.2199152Quantized consensus is an important problem due to that digital communications are widely adopted and has attracted recurring interest([15]–[24]).Kashyap et al.([15])developed an average-consensus algorithm with integer-valued states, which can ensure the asymptotic convergence of agents’states to an integer approximation of the average of the initial states. They gave an upper bound for the expected convergence time for fully connected networks and linear networks.Frasca et al. ([19]),Carli et al.([20]),and Li et al.([24])considered the av-erage-consensus problem with real-valued states and quantized communications.In[19]and[20],static uniform quantizers and dynamic logarithmic quantizers with an infinite number of quantization levels were considered,respectively.In[20]and [24],average-consensus algorithms with dynamicfinite-level uniform quantizers were proposed.Especially,in[24],it is shown that if the network is connected,then the control param-eters can be properly chosen such that the average-consensus can be achieved with an exponential convergence rate by using a single-bit quantizer.The work of[24]was extended to the cases with link failures in[25]and time-delay in[26], respectively.The aforementioned works are concerned with thefirst-order integrator systems with measurable states.In many applications, however,we encounter higher order systems with partially mea-surable states.Dynamic output feedback control of multi-agent systems of general higher order dynamics wasfirst studied by Fax and Murray([3]).Tuna proposed a controller design algo-rithm for synchronization of discrete-time linear systems based on static relative output feedback([27]).Qu et al.([28])dealt with static output feedback of multi-agent systems via feedback linearization,where the control input of an agent is given in terms of its own output and the relative output errors with re-spect to its neighbors.Li et al.([29])and You and Xie([30]) considered distributed coordination based on dynamic relative output feedback.Hong et al.([31])developed a distributed ob-server for leader-following systems where the leader and the followers are described by second-order integrators and each follower constructs a state observer based on the leader’s posi-tion,neighbors’positions and leader’s control input to estimate the leader’s velocity.More literature on distributed observers can be found in[32]and[33].In this paper,we consider distributed coordination of multi-agent networks based on digital communications.The communications among agents are described by an undirected graph.Each agent is described by a discrete-time second-order integrator,with measurable position but unmeasurable velocity, unlike[20]and[24].Since the states of the agents are only partially measurable,the encoding-decoding scheme in[24]0018-9286/$31.00©2012IEEEcan not be easily extended to this case.Further,unlike[20] where infinite-level logarithmic quantizers are considered,we aim to design an efficient encoding-decoding scheme under a limited data rate for information exchange between agents. Ourfirst challenge is to jointly design state-observation and encoding-decoding for communication and computation effi-ciency while achieving consensus.Note that one natural idea is to design a state-observer for each agent and then encode and transmit the state-estimate to neighbors,which,however, requires a distributed control with complex encoding-decoding scheme in order to eliminate the effect of quantization and estimation errors on thefinal closed-loop system.Further,even such a control scheme can be developed to guarantee conver-gence,the computation and communication loads are generally higher and the performance(i.e.,the convergence rate under the same bit rate)is not definitely better.From the perspective of minimizing communication bit rate and reducing computation load,we propose an integrative ap-proach for observer and encoder-decoder design in this paper. At each time instant,the quantized innovation of each agent’s position is sent to its neighbors,while,at each receiver,an ob-server-based decoder is activated to obtain an estimate of the sender’s position and velocity.Our design can result in a much lower communication requirement due to:1)the encoder inputs,i.e.,agents’positions,contains less variables than the full states;2)the encoder outputs are in fact a kind of quantized innova-tions of agents’positions and it is known that innovations gen-erally can be quantized with much lower numbers of bits than the positions themselves.It is worth pointing out that even if the quantization is ignored,our encoders and decoders are different from the dynamic feedback control law in[3].Here,we do not design a state observer for each agent separately,but send the quantized innovation of each agent’s output directly and inte-grate the state observation and communication process together. Our observer-based encoding-decoding scheme is also different from the distributed observer given in[31],especially,we do not require the knowledge of the other agents’control inputs.We develop a distributed coordinated control law by using the states of the decoders and encoders,provide sufficient con-ditions on the control gains and network topology for the ex-istence offinite-level quantizers to ensure the closed-loop con-vergence,and show that these conditions are also necessary in some sense.We prove that,by selecting the number of quantiza-tion levels(data rate)properly,the asymptotic synchronization of the positions and velocities can be achieved.Furthermore,for a connected network,we can always select the control gains, such that2-bit quantizers can guarantee the exponential conver-gence of the closed-loop system and the convergence rate can be predesigned.It should be noted that compared with classical non-quan-tized and centralized state observers,due to the nonlinearity of the quantization and the coupling of all agents’states,the con-vergence of a given observer-based encoding-decoding scheme depends on the control inputs of all agents and the closed-loop dynamics of the whole network.Different from[24],the rela-tionship between the estimation error and the quantization error does not have a simple form if observer type is not properly se-lected,and it is very difficult to get an explicit expression for the relationship between the spectral radius of the closed-loop state matrix and the eigenvalues of the graph Laplacian.All these significantly complicate the closed-loop analysis and the con-trol parameter selection.Also,different from[24],there is no explicit relationship between the stability margin and the con-trol gain,which makes the performance limit analysis difficult. By using differential calculus and limit analysis,we give a linear approximation of the spectral radius of the closed-loop state ma-trix with respect to the control gain ratio and algebraic connec-tivity of the communication graph,based on which,a relation-ship between the performance limit and the parameters of the network and system is revealed.We show that as the number of agents increases to infinity,the asymptotic highest convergence rate is when using a-level quantizer,where is the ratio of the algebraic connectivity to the spectral radius of the Laplacian matrix of the communica-tion graph.The remainder of this paper is organized as follows.In Section II,we present the model of the network and agents,give the structures of observer-based encoders,observer-based de-coders and distributed coordinated control laws.In Section III, we analyze the closed-loop system and give conditions on the network topology,the control gains and the number of quantization levels to ensure convergence.In Section IV,we discuss the selection of the control gain ratio and show that2-bit quantizers can guarantee the convergence of the closed-loop system by selecting the control gains properly.We also give an explicit form of the asymptotic convergence rate.In Section V, we draw some concluding remarks and propose future research topics.The following notation will be used throughout this paper: denotes a column vector with all ones.denotes the identity matrix with an appropriate size.For a given set,the number of its elements is denoted by.For a given vector or matrix ,we denote its transpose by,its-norm by,its Euclidean norm by,its spectral radius by,and its trace by.For a given positive number,the natural logarithm, the logarithm of with base2,the maximum integer less than or equal to,and the minimum integer greater than or equal to are respectively denoted by,,and.II.P ROBLEM F ORMULATIONA.Agent and Network ModelsWe consider distributed coordination of a network of agents with the second-order dynamics:(1) where,,and are the position, velocity control of the th agent,respectively.Here, is the output of agent,that is,for agent,only its po-sition is measurable.The agents communicate with each other through a network whose topology is modeled as an undirected graph,where the agents and the communication channels between agents are represented by the node set and the edge set,respectively.The weighted adjacency matrix ofLI AND XIE:DISTRIBUTED COORDINATION OF MULTI-AGENT SYSTEMS WITH QUANTIZED-OBSERVER BASED ENCODING-DECODING3025is denoted by.Note that is a sym-metric matrix.An edge by the pair represents a communication channel from to and if and only if.The neighborhood of the th agent is denoted by.For any,,and if and only if.Also,is called the degree of,and is called the degree of.The Laplacian matrix of is defined as,where.The Laplacian matrix is a sym-metric positive semi-definite matrix and its eigenvalues in an ascending order are denoted by,where is the spectral radius of and is called the algebraic connectivity of([34],[35]).A sequence of edges is called a path from node to node.The graph is called a connected graph if for any ,there is a path from to.B.Observer-Based Encoding-DecodingWe consider digital communication channels with limited channel capacity.At each time step,what each agent can send to its neighbors is only a coded version of its current and past measurements.Generally speaking,the encoder of the th agent may take the following form:(2) where and are the output and input of the encoder, respectively,is a Borel measurable function and is a quan-tizer.Note that both the structure and parameters of and may be time-varying and the encoder may have infinite memory. In this paper,we propose afinite memory encoder of agent as(3) where is an exponentially decaying scaling function to be defined later.In the above,and are the internal states of the encoder and is afiquantizer given by(4) where is the number of quantization levels of.After is received by one of the th agent’s neighbors,say agent,a decoder will be activated:(5) where and are the outputs of the decoder.Remark1:In the above,is a quantized innovation with scaling.From the dynamic(1)of the th agent,we know that to get estimates for and,following the standard observer design,the decoder can be in the form(6) where and are the observer gains.It can be easily verified that if and the quantizer is the identity function,then(6)degenerates to the classical deadbeat posterior state observer based on output.However,since is not available for the neighbors of the th agent,we adopt decoder(5)instead.Remark2:From(3)and(5),we have(7) We will show that and can be viewed as the estimates for and,respectively.Denoteas the quantization error in encoder,as the estimation error for andas the estimation error for.By(3)and some direct calculation,we get(8) and(9) It can be seen that if the quantization error is bounded, then due to the vanishing of,the estimation errorsand will both to zero asymptotically as.Note that here,for the velocity estimation,there is one step delay.Remark3:The relationship among the estimation errors ,and the quantization error is not asin thefirst-order case It will be seen later that(8)and(9)will play an important role in the closed-loop analysis.Observe that the estimation errors for velocities depend on two steps of quantization errors,which, as we can see later,leads to an additional bit required for the quantizers as compared to thefirst-order case([24]). Remark4:From the above,we can see that both the en-coder(3)and the decoder(5)can be viewed as the state ob-servers based on the output and the quantized innovation. We call the encoder(3)an observer-based encoder and the de-coder(5)an observer-based decoder.Though the velocityis not measurable,the th agent and its neighbors can make an estimate for the overall state by using an ob-server-based encoder and an observer-based decoder.At each time step,each agent only needs to send the quantized innova-tion of its output to its neighbors,then the neighbors can use observer-based decoders to get estimates for the state of the3026IEEE TRANSACTIONS ON AUTOMATIC CONTROL,VOL.57,NO.12,DECEMBER2012agent.However,generally speaking,there is no separation prin-ciple for the encoder-decoder design and the control design. Compared with classical non-quantized and centralized state observers,due to the nonlinearity of the quantization and the coupling of all agents’states,the convergence of a given ob-server-based encoding-decoding scheme depends on the control inputs of all agents and the closed-loop dynamics of the whole network,which significantly complicates the analysis as seen below.C.Distributed Control LawIn this paper,we aim at designing a distributed coordinated control law based on quantized communications such that(10) We propose a distributed coordinated control law of the form(11) where and are the control gains.From(3),(5)and(11),we can see that the control input of each agent only depends on the state of its own encoder and the states of the decoders associated with the channels from its neighbors.Remark5:Since the states of agents are only partially mea-surable,the encoding-decoding scheme in[24]where agents of single integrator dynamics are considered cannot be easily extended to this case.The challenge is to design state observers and encoders-decoders jointly so that they can achieve con-sensus with efficient communications and computation.One natural idea is to design a state-observer for each agent and then encode and transmit the state estimate to neighbors.For example,we may adopt the following state-observer for the th agent:(12)is then encoded and transmitted to the neigh-bors of the th agent.However,since the control inputand estimation error are not available for its neighbors,to eliminate the effect of quantization and estima-tion errors on thefinal closed-loop system,we may need a more complex encoding-decoding scheme and a control law than(3), (5)and(11).Further,even if we canfind such a scheme to guar-antee convergence,the computation and communication loads are higher and the performance(i.e.,the convergence rate under the same bit rate)is not definitely better.From the perspective of bit rate constraint and reducing computation load,we propose an integrative approach for the state-observer and encoder-de-coder design.III.C ONVERGENCE A NALYSISThis section is devoted to the convergence analysis of the proposed distributed control law in the last section.To this end, we introduce the following notation:where.We also define the unitary matrix(13) where is the unit eigenvector of associated with,that is,,,.Under the protocol(3),(5)and(11),due to the quantization, the closed-loop system is a nonlinear discontinuous system. Generally speaking,the convergence analysis is difficult, however,by using the estimation error expressions(8)and (9),the closed-loop equation can be converted into a linear equation with time-varying disturbances,whose homogeneous part is just the closed-loop equation without quantization.Then by properly selecting the number of quantization levels,the quantizers can be kept unsaturated and the convergence of the closed-loop system can be achieved.We make the following assumptions.A1)There are known positive constants,,,, such that,,,.A2)The communication graph is connected.A3).A4).The following lemma,whose proof can be found in Ap-pendix,will be used in the analysis of the homogeneous part of the closed-loop system.Lemma3.1:Let(14) Then,i),if and only if As-sumptions(A2)–(A4)hold.ii)Let(15)LI AND XIE:DISTRIBUTED COORDINATION OF MULTI-AGENT SYSTEMS WITH QUANTIZED-OBSERVER BASED ENCODING-DECODING3027If Assumptions(A3)–(A4)hold,then the eigenvalues of are0,,and ,where(16) In the above,the arguments,of and were omitted,and,where.From Lemma 3.1,we know that if Assumptions (A2)–(A4)hold,then is diagonalizable.Let ,,be nonsingular matrices,such that whereDenote,. In the following,the dependence of,and on and will be omitted when there is no confusion.The following theorem gives sufficient conditions on the con-trol gains and network topology for the existence offinite-level quantizers to ensure the closed-loop convergence.Theorem3.1:Suppose Assumptions(A1)–(A4)hold.Let the scaling function,where(17) and.If the numbers of quantization levels of the quantizer,satisfy(18) and(19)where, then under the protocol(3),(5)and(11),the closed-loop system satisfies(20) Furthermore,the convergence rate is given by(21)Proof:The proof can be divided into three steps.First, we convert the closed-loop system into non-coupled linear equations with nonlinear disturbances.The disturbances are combinations of the estimation errors which are related to the quantization errors as observed from by(8)and(9).Second, we estimate the bound of the synchronization errors in terms of the quantization errors and system and control parameters. Finally,we prove the boundness of the quantization error by properly choosing the control parameters and the number of quantization levels,which will lead to the convergence of the closed-loop system.Step1)From(7)and(11),it follows that(22) Substitute the control law above into the system(1),we haveLet,,where is defined in(13).Denote the th components of and by and,respectively.Then we have, and(23) Denote,then the(23)can be rewritten as(24) where with.It is clear that to get(20),we only need to prove,.3028IEEE TRANSACTIONS ON AUTOMATIC CONTROL,VOL.57,NO.12,DECEMBER2012Step2)By(24),we have(25) Further,by(8)and(9),noting that,we haveThen it follows from(25)that(26) By the definition of,and,we get(27) Step3)By Lemma A.2,we get.This together with(26)gives,, which further implies(20).Then from, (26)and(27),we get(21).Observe that the distributed control law in Theorem3.1re-lies on,which requires each agent to know the graph and may not be practical.This restriction is relaxed by the following corollary.Corollary3.1:Suppose Assumptions(A1)–(A4)hold.Let the scaling function,where(28) and.If the numbers of quantization levels of the quantizer,satisfy(29) and(30) where then under the protocol(3),(5)and(11),the closed-loop system satisfies(31) and the convergence rate is given by(32)Proof:Noting that and,by Theorem3.1,we get the conclusion of this corollary.Remark6:From Theorem3.1and Corollary3.1,we can see that the convergence factor can be properly chosen to tune the convergence rate of the closed-loop system.By Corollary3.1, we may select the control parameters by the following steps.i)Choosing,such that Assumptions(A3)–(A4)hold.ii) Choosing and then according to(28).iii)Choosing the number of quantization levels according to(29)and(30). Remark7:Corollary3.1tells us that to select proper and the number of quantization levels,we do not need to know, that is,the exact Laplacian matrix.Furthermore,Assumption A4)holds if,so the selection of the con-trol gains may not need the knowledge of.However,from the definition of,we can see that the selection of needs the knowledge of the eigenvalues of the Laplacian ma-trix.Hence,we still need some global knowledge of the net-work topology to select the control parameters.In the case when the network topology can be predesigned,this is not a problem. However,in some applications,the network topology may notLI AND XIE:DISTRIBUTED COORDINATION OF MULTI-AGENT SYSTEMS WITH QUANTIZED-OBSERVER BASED ENCODING-DECODING3029be known to each agent,for example,under switching topolo-gies due to changing environment.In this situation,the problem of estimating the eigenvalues of the Laplacian matrix in a dis-tributed manner becomes relevant.Franceschelli et al.([36]) gave an algorithm to estimate the eigenvalues of a Laplacian matrix by each agent using the fast Fourier transform.The com-bination of the eigenvalue estimation algorithm with our pro-posed distributed coordinate control algorithm is an interesting future research topic.Remark8:From Lemma3.1and the proof of Theorem3.1, we can see that A2-A4)are necessary and sufficient for the sta-bility of the homogeneous part of the closed-loop systems(24). Since,we can see that a smaller degree, which implies lower local connectivity,will instead give more flexibility for selecting the control gains.In the main theorem of[15](Theorem1of[15]),the authors proved that under their algorithm,as time goes on the states of agents converge to a ball centered at the average of the initial states with radius less than or equal to the quantization interval, with probability1.They also proved that there always exists a finite time such that the states of the agents enter and stay in the ball with a positive probability when.An upper bound for the mathematical expectation of the convergence time for fully connected networks and linear networks was also pro-vided.In this paper,we focus on the case with real-valued states and the asymptotic convergence to exact synchronization.The algorithm given here can guarantee convergence to synchro-nization with an arbitrary precision as time goes on.In the fol-lowing,we will give an analysis on the convergence time for a given precision for connected networks.For any given, denote and,which are respectively the time for the positions and veloci-ties of all the agents with precision.Theorem3.2:Suppose the conditions of Theorem3.1hold,and.Then under the protocol(3),(5)and(11),for sufficiently small, the convergence time for the position and velocity respectively satisfies(33) whereProof:The proof can be found in the Appendix. Remark9:Similar to Corollary 3.1,the constantin Theorem 3.2can be replacedby Fig.1.Curves of of Example1.,which gives us a relationship between the upper bound of the convergence time and the number of agents.IV.P ARAMETER D ESIGN AND P ERFORMANCE L IMIT A NALYSIS In this section,we shall investigate controller parameter se-lection and analyze the asymptotic consensus convergence rate.A.Selecting the Control Gain RatioSelecting the control gains and is equivalent to selecting a control gain ratio and the position control gain. It is easily seen that Assumptions A3)-A4)hold if and only if and.Further will max-imize,which implies the largest stability margin of the homogeneous part of the closed-loop system(24).1)Example1:We consider a10-node network withand.The curves of with respect to with different control gain ratios are shown in Fig.1.It can be seen that will go to1as or,andfirst decreases and then increases with respect to.The of the inflection point of reaches its maximum when.Further,it can be proved theoretically that when is sufficiently small, is almost a linear,monotone decreasing function of .We have the following result.Lemma4.1:If Assumptions A2)-A4)hold,then for any given ,we have(34)Proof:The proof can be found in Appendix.For Example1,the curves of andwith different are shown in Fig.2.B.Selecting the Control Parameters Under a Given Communication Data RateIn Theorem3.1,we give a criterion for selecting the number of quantization levels(communication data rate)under given control gains and a convergence rate.In the following theorem,3030IEEE TRANSACTIONS ON AUTOMATIC CONTROL,VOL.57,NO.12,DECEMBER2012Fig.2.Curves of and of Example1with different,where dot are for and the solid lines are for.we will consider how to select the control parameters under a given communication data rate.Theorem4.1:Suppose Assumptions A1)and A2)hold.For any given,,denote(35) Then,i)is nonempty.ii)If,,and the numbers of the quantization levels of satisfy(36)then under the protocol given by(3),(5)and(11)with,the closed-loop system satisfieswhere is a constant satisfying(37)Proof:From Lemma4.1,we have(38) which impliesFrom the aforementioned,noting that the ex-ists,and(35),we have(i).For any given integer and constant,if ,,(36)and(37)hold,then it is easily verified that,Assumptions A3)-A4)and(18)hold. Then noting that and,we know that(17)and(19)also hold.By Theorem3.1,we get ii).Remark10:In[24],it is shown that for a connected network withfirst-order agents,average-consensus can be achieved with an exponential convergence rate based on merely1-bit informa-tion exchange between agents.Here,we prove that for the case with second-order agents,2-bit quantizers suffice for the expo-nential asymptotic synchronization of agents’pared with[24],from(A.2),we can see that the additional bit is used to overcome the uncertainty in estimating the velocity of the agent.Remark11:Compared with[24],the performance limit analysis for the second order agents with partial measur-able states is much more challenging.In[24],the spec-tral radius of the closed-loop matrix has the simple form:,where is the control gain.In this paper,it is very difficult to get an explicit expression for the relationship between the closed-loop spectral radiusand the eigenvalues of the Laplacian matrix.By differential mean theorem and limit analysis,we develop Lemma4.1to give a linear approximation of with respect to the control gains and the algebraic connectivity.From(38),we can see that Lemma4.1plays a vital role in establishing Theorem 4.1.Different from[24],there is also no explicit relationship between the stability margin and the control gain ,which also poses a significant challenge in the asymptotic convergence rate analysis as seen later in Section IV-C.1)Example2:We consider a network with10nodes andweights,which means that,if,other-wise,.The edges of the graph are randomly generated according to,for any unordered pair. Here,,.The initial states are chosen as and,.The con-trol gain and,which give.The scaling factor is taken as 0.9998.According to Theorem3.1,the2-bit quantizer can be used.The evolution of the states is shown in Fig.3.It can be seen that both the positions and the velocities of the agents are asymptotically synchronized.Next,we set.In this。
Some properties of complex matrix-variate generalized Dirichlet integrals
a r X i v :m a t h /0607625v 1 [m a t h .L O ] 25 J u l 2006Proc.Indian Acad.Sci.(Math.Sci.)V ol.115,No.3,August 2003,pp.241–248.Printed in IndiaSome properties of complex matrix-variate generalized Dirichlet integralsJOY JACOB,SEBASTIAN GEORGE and A M MATHAI ∗Department of Statistics,St.Thomas College,Arunapuram P.O.,Palai,Kottayam 686574,India ∗McGill University,Montreal,Canada and Centre for Mathematical Sciences,Pala Campus,Arunapuram P.O.,Pala 686574,India E-mail:jjstc@sancharnet.in MS received 9October 2003;revised 14October 2004Abstract.Dirichlet integrals and the associated Dirichlet statistical densities are widely used in various areas.Generalizations of Dirichlet integrals and Dirichlet mod-els to matrix-variate cases,when the matrices are real symmetric positive definite or hermitian positive definite,are available [4].Real scalar variables case of the Dirichlet models are generalized in various directions.One such generalization of the type-2or inverted Dirichlet is looked into in this article.Matrix-variate analogue,when the matri-ces are hermitian positive definite,are worked out along with some properties which are mathematically and statistically interesting.Keywords.Beta integrals;gamma integrals;complex matrix-variate beta random variables;type-2Dirichlet model.1.Introduction This paper deals with probability densities on the space of matrices.All the matrices appearing in this article are p ×p hermitian positive definite unless specified otherwise.X ,Y ,...will denote matrices whose elements are functionally independent real scalar mathematical variables or random variables.˜X ,˜Y ,...will denote matrices whose elements are in the complex domain.Constant matrices will be denoted by A ,B ,...whether the elements are real or complex.tr (·)will denote the trace of the matrix (·),|(·)|will denote the determinant as well as absolute value of (·)and |det (·)|will denote the absolute value of the determinant of (·).Transpose will be denoted by a prime,complex conjugate by a bar,conjugate transpose by a star.Thus ˜X=˜X ∗>0will mean the hermitian matrix ˜X is positive definite.d X will indicate the wedge product of differentials in X .For example,when X =(x i j )and real,d X =∧i ,j d x i jwhen all x i j ’s are distinct=∧i ≥j d x i j =∧i ≤j d x i jwhen X =X ′. 0<˜X =˜X ∗<I (·)d ˜X indicates the integral of (·)over all ˜X such that ˜X =˜X ∗>0as well as I −˜X >0.In other words,all eigenvalues of ˜X are in the open interval (0,1),where I denotes an identity matrix.For any positive definite hermitian matrix A we denote by A 1242Joy Jacob,Sebastian George and A M MathaiIn our discussions,we need a few Jacobians of matrix transformations and integrals over real scalar functions of matrix arguments.Thesewill be stated here without proof.For proofs and other details,see [4].˜Y =A ˜XA ∗,|A |=0⇒d ˜Y=|det (A )|2p d ˜X =|det (AA ∗)|p d ˜X ,(1.1)where A is a constant matrix (p.183of [4])˜Y =˜X −1,|˜X |=0⇒d ˜Y =|det (˜X )|−2p d ˜X for ˜X =˜X ∗,(1.2)(p.190of [4]).Let ˜T =(˜t i j ),˜t i j =0for i <j ,˜t j j =t j j >0(real and positive)for j =1,...,p .That is,˜Tis a lower triangular matrix with real positive diagonal elements.Then ˜X =˜T ˜T ∗⇒d ˜X =2p p ∏j =1t 2(p −j )+1j j d ˜T .(1.3)The complex matrix-variate gamma,denoted by ˜Γp (α),and the gamma integral are defined as follows:˜Γp (α)=πp (p −1)˜Γp (α+β),ℜ(α)>p −1,ℜ(β)>p −1(1.6)=0<˜X =˜X ∗<I |det (˜X )|α−p |det (I −˜X )|β−p d ˜X (1.7)= ˜X =˜X ∗>0|det (˜X)|α−p |det (I +˜X )|−(α+β)d ˜X .(1.8)The functions of ˜X appearing in (1.7)and (1.8),divided by the normalizing constant ˜Bp (α,β),produce the complex matrix-variate type-1and type-2beta densities respec-tively (p.357of [4]).Dirichlet integrals and the associated Dirichlet densities come in naturally in order statistics problems,in reliability analysis and in certain survival anal-ysis problems.In Bayesian statistical analysis a Dirichlet model is usually taken as the prior distribution for multinomial probabilities.In random division and certain geometri-cal probability problems Dirichlet model comes in naturally,see for example [5].Dirichlet model is extended to matrix-variate Liouville type by [2].See [1]for some applications in engineering,[3]for statistical applications and [6]for some physics problems.The model that we are going to deal with in the present article is the complex matrix-variate analogue of an extended inverted or type-2Dirichlet model incorporating succes-sive sums of the variables into it.Complex matrix-variate beta random variables243 2.Matrix-variate analogue of an extended Dirichlet modelLet˜X1,...,˜X k be hermitian positive definite p×p matrix random variables having the joint density functionf(˜X1,...,˜X k)=c k|det(˜X1)|α1−p···|det(˜X k)|αk−p×|det(I+˜X2+···+˜X k)|β1···|det(I+˜X k)|βk−1×|det(I+˜X1+···+˜X k)|−(α1+···+αk+1+β1+···+βk)(2.1) forℜ(αj)>p−1,ℜ(αj+1+···+αk+1+βj+···+βk)>p−1,j=1,...,k,and f(˜X1,...,˜X k)=0elsewhere,where c k is the normalizing constant.We will study some properties of(2.1)in the present article.Since˜X1··· ˜X k f(˜X1,...,˜X k)d˜X1∧...∧d˜X k=1,we can evaluate c k by successive integration starting with˜X1.Forfixed˜X2,...,˜X k let L1= ˜X1=˜X∗1>0|det(˜X1)|α1−p×|det(I+˜X1+···+˜X k)|−(α1+···+αk+1+β1+···+βk)d˜X1.Note that|det(I+˜X1+···+˜X k)|=|det(I+˜X2+···+˜X k)|×|det[I+(I+˜X2+···+˜X k)−1/2טX1(I+˜X2+···+˜X k)−1/2]|.Now,make the transformation˜Y1=(I+˜X2+···+˜Xk)−1/2˜X1(I+˜X2+···+˜Xk)−1/2.Thend˜Y1=|det(I+˜X2+···+˜X k)−p|d˜X1.Substituting for˜X1in terms of˜Y1we haveL1=|det(I+˜X2+···+˜X k)|−(α2+···+αk+1+β1+···+βk)× ˜Y1=˜Y∗1>0|det(˜Y1)|α1−p|det(I+˜Y1)|−(α1+···+αk+1+β1+···+βk)d˜Y1=|det(I+˜X2+···+˜X k)|−(α2+···+αk+1+β1+···+βk)טΓp(α1)˜Γp(α2+···+αk+1+β1+···+βk)˜Γp(αj+···+αk+1+βj+···+βk)(2.2)244Joy Jacob,Sebastian George and A M Mathaiforℜ(αj)>p−1,ℜ(αj+1+···+αk+1+βj+···+βk)>p−1,j=1,...,k.We can look into some interesting results and the corresponding matrix transforma-tions.These will be stated as theorems.We need two more results in order to establish our main results.These will be listed as lemmas.Lemma2.1.Let˜X and A be p×p hermitian positive definite matrices.Then˜Y1= A1/2˜XA1/2and˜Y2=˜X1/2A˜X1/2have the same eigenvalues.This can be easily seen by looking at the determinantal equations for the eigenvalues |A1/2˜XA1/2−λI|=0⇒|˜XA−λI|=0⇒|˜X1/2A˜X1/2−λI|=0.Thus,˜Y1and˜Y2have the same equations giving rise to the same eigenvaluesλ1,...,λp,λj>0,j=1,...,p.Lemma2.2.Let the common real eigenvalues of˜Y1and˜Y2of Lemma2.1be distinct.Then the wedge product d˜Y1=d˜Y2in the integrals.Proof.Let˜U and˜V be unitary matrices with real diagonal elements such that˜W1=˜U∗˜Y1˜U=D=diag(λ1,...,λp)=˜V∗˜Y2˜V=˜W2.Then from Theorem4.4of[4]d˜Y1=d˜W1= ∏j>k|λk−λj|2 d D∧d˜G1andd˜Y2=d˜W2= ∏j>k|λk−λj|2 d D∧d˜G2,where d˜G1and d˜G2are the following:d˜G1=∧[˜U(d˜U)]and d˜G2=∧[˜V(d˜V)].(d˜U)and(d˜V)denote the matrices of differentials(entry-wise derivatives)in˜U and˜V respectively.Now from Corollary4.3.1of[4]˜O1(p)d˜G1= ˜O1(p)d˜G2=πp(p−1)Complex matrix-variate beta random variables245 Proof.From the transformation in(2.3),we haveI−˜Y1=I−(I+˜X1+···+˜X k)−1/2˜X1(I+˜X1+···+˜X k)−1/2=(I+˜X1+···+˜X k)−1/2[(I+˜X1+···+˜X k)−˜X1]×(I+˜X1+···+˜X k)−1/2=(I+˜X1+···+˜X k)−1/2(I+˜X2+···+˜X k)×(I+˜X1+···+˜X k)−1/2I−˜Y2=(I+˜X2+···+˜X k)−1/2(I+˜X3+···+˜X k)×(I+˜X2+···+˜X k)−1/2I−˜Y k−1=(I+˜X k−1+˜X k)−1/2(I+˜X k)(I+˜X k−1+˜X k)−1/2I−˜Y k=(I+˜X k)−1.(2.4) From the above representations of I−˜Y1,...,I−˜Y k,from(1.1),(1.2)and from Lemma2.2, we can evaulate the Jacobian of the transformation in(2.3)from(2.4).d˜Y1=d[(I+˜X1+···+˜X k)−1/2(I+˜X2+···+˜X k)×(I+˜X1+···+˜X k)−1/2]=d[(I+˜X2+···+˜X k)1/2(I+˜X1+···+˜X k)−1×(I+˜X2+···+˜X k)1/2]=|det(I+˜X2+···+˜X k)|p|det(I+˜X1+···+˜X k)|−2p d˜X1forfixed˜X2,...,˜X k.Similarly,d˜Y2=|det(I+˜X3+···+˜X k)|p|det(I+˜X2+···+˜X k)|−2p d˜X2andfinally,d˜Y k=|det(I+˜X k)|−2p d˜X k.Since the transformation in(2.3)is of a triangular nature,the Jacobian matrix will be a triangular block matrix with the Jacobian being the product of the determinants of the diagonal blocks and the Jacobian is given by,ignoring the sign,d˜Y1∧...∧d˜Y k=|det(I+˜X1+···+˜X k)|−2p |det(I+˜X2+···+˜X k)|−p···|det(I+˜X k)|−p d˜X1∧...∧d˜X k.(2.5)246Joy Jacob,Sebastian George and A M MathaiFrom (2.3),(2.4)and (2.5)we can compute the following product:k ∏j =1|det (˜Yj )|αj −p |det (I −˜Y j )|αj +1+···+αk +1+βj +···+βk −p d ˜Y 1∧...∧d ˜Y k = k∏j =1|det (˜X j )|αj −p |det (I +˜X 2+···+˜X k )|β1×|det (I +˜X3+···+˜X k )|β2...|det (I +˜X k )|βk −1×|det (I +˜X1+···+˜X k )|−(α1+···+αk +1+β1+···+βk )d ˜X 1∧...∧d ˜X k .(2.6)Multiplying (2.6)on both sides by c k we have the result since the right-hand side with c k is the density in (2.1)and the left-hand side with c k is the product of complex matrix-variate type-1beta densities.It is easy to see that the converse also holds.Theorem 2.2.Let the hermitian positive definite matrices ˜Y 1,...,˜Y k be independently dis-tributed as complex matrix-variate type-1beta random variables where ˜Yj has the param-eters (αj ,αj +1+···+αk +1+βj +···+βk )for j =1,...,k.Consider the transformation in (2.3)on the space of k-tuples of hermitian positive definite matrices ˜X 1,...,˜X k .Then ˜X1,...,˜X k have the joint density as given in (2.1).Thus Theorems 2.1and 2.2also provide a characterization of the density in (2.1).It is known that when ˜Yj has a complex matrix-variate type-1beta density,then I −˜Y j again has a complex matrix-variate type-1beta density.Thus,from Theorems 2.1and 2.2we can get two more results as corollaries.One of them will be listed here as a theorem and it can also be proved independently by proceeding parallel to the proof in Theorem 2.1.Theorem 2.3.Let ˜X1,...,˜X k have the joint density in (2.1).Consider the transformation ˜Z1=(I +˜X 1+···+˜X k )−1/2(I +˜X 2+···+˜X k )(I +˜X 1+···+˜X k )−1/2˜Z2=(I +˜X 2+···+˜X k )−1/2(I +˜X 3+···+˜X k )(I +˜X 2+···+˜X k )−1/2...˜Z k =(I +˜X k )−1.(2.7)Then ˜Z 1,...,˜Z k are independent complex matrix-variate type-1beta random variables with ˜Zj having the parameters (αj +1+···+αk +1+βj +···+βk ,αj ),for j =1,...,k.Theorem 2.4.Let ˜X1,...,˜X k have the joint density in (2.1).Consider the transformation ˜U 1=˜X −1/21(I +˜X 2+···+˜X k )˜X −1/21˜U 2=˜X −1/22(I +˜X 3+···+˜X k )˜X −1/22...˜U k =˜X −1k .(2.8)Then ˜U 1,...,˜U k are independent complex matrix-variate type-2beta random variables with ˜Uj having the parameters (αj +1+···+αk +1+βj +···+βk ,αj ),for j =1,...,k.Complex matrix-variate beta random variables247 Proof.From eqs(2.8),(1.1),(1.2)and Lemma2.2we have the following:d˜U1=|det(I+˜X2+···+˜X k)|p|det(˜X1)|−2p d˜X1forfixed˜X2,...,˜X k.d˜U2=|det(I+˜X3+···+˜X k)|p|det(˜X2)|−2p d˜X2,andfinallyd˜U k=|det(˜X k)|−2p d˜X k.Since the transformation in(2.8)is of a triangular nature,we have the Jacobian given by d˜U1∧...∧d˜U k=|det(˜X1)|−2p...|det(˜X k)|−2p|det(I+˜X2+···+˜X k)|p×|det(I+˜X3+···+˜X k)|p...|det(I+˜X k)|p×d˜X1∧...∧d˜X k.(2.9) From(2.8),I+˜U1=I+˜X−1/21(I+˜X2+···+˜X k)˜X−1/21=˜X−1/21[˜X1+(I+˜X2+···+˜X k)]˜X−1/21=˜X−1/21(I+˜X1+···+˜X k)˜X−1/21I+˜U2=˜X−1/22(I+˜X2+···+˜X k)˜X−1/22.. .I+˜U k=˜X−1/2k (I+˜X k)˜X−1/2k.(2.10)Now from(2.8),(2.9)and(2.10)we havek∏j=1|det(˜U j)|αj+1+···+αk+1+βj+···+βk−p×|det(I+˜U j)|−(αj+···+αk+1+βj+···+βk) d˜U1∧...∧d˜U k= k∏j=1|det(˜X j)|αj−p |det(I+˜X2+···+˜X k)|β1···|det(I+˜X k)|βk−1×|det(I+˜X1+···+˜X k)|−(α1+···+αk+1+βk+···+βk)d˜X1∧...∧d˜X k.(2.11) Multiply both sides of(2.11)by c k to see the result.The converse also holds.Thus Theorem2.4and its converse also provide a charac-terization of the density in(2.1).It is known that when˜U j has a complex matrix-variate type-2beta distribution then˜U−1j has a complex matrix-variate type-2beta distribution with the parameters interchanged.This property also gives a couple of results.Instead of ˜U−1j,a slightly different transformation will be considered in the next theorem.248Joy Jacob,Sebastian George and A M MathaiTheorem2.5.Let˜X1,...,˜X k have the joint distribution as given in(2.1).Consider the transformation˜V1=(I+˜X2+···+˜Xk)−1/2˜X1(I+˜X2+···+˜Xk)−1/2˜V2=(I+˜X3+···+˜Xk)−1/2˜X2(I+˜X3+···+˜Xk)−1/2.. .˜Vk−1=(I+˜Xk)−1/2˜Xk−1(I+˜Xk)−1/2˜Vk=˜X k.Then˜V1,...,˜V k are independent complex matrix-variate type-2beta random variables with˜V j having the parameters(αj,αj+1+···+αk+1+βj+···+βk),for j=1,...,k. The proof can be given by using the steps parallel to the ones in the proof of Theo-rem2.1.The converse of Theorem2.5is also true.Further,Theorem2.5and its converse also provide a characterization for the density in(2.1).By exploiting the relationships between complex matrix-variate type-1and type-2beta random variables one can derive a number of results and a number of interesting matrix transformations.These will not be enumerated here in order to save space.AcknowledgementsThe last author would like to thank the Natural Sciences and the Engineering Research Council of Canada forfinancial assistance.The authors would like to express their sincere thanks to the referee for making many valuable suggestions which enabled the authors to make the presentation far better.References[1]Biyari K H and Lindsey W C,Statistical distributions of hermitian quadratic form incomplex Gaussian variables,IEEE rmation Theory39(3)(1991)1076–1082 [2]Gupta R D and Richards D St P,Multivariate Liouville distributions,J.MultivariateAnal.23(1987)233–256[3]Hayakawa T,On the distribution of latent roots of a complex Wishart matrix(non-centralcase),Ann.Inst.Stat.Math.24(1972)1–17[4]Mathai A M,Jacobians of matrix transformations and functions of matrix argument(New York:World Scientific Publishing)(1997)[5]Mathai A M,An introduction to geometrical probability:Distributional aspects withapplications(New York:Gordon and Breach Publishers)(1999)[6]Mehta M L,Random matrices and statistical theory of energy levels(New York:Aca-demic Press)(1967)。
常见限制性内切酶识别序列
常见限制性内切酶识别序列(酶切位点)(BamHI、EcoRI、HindII I、NdeI、XhoI等)Time:2009-10-22 PM 15:38Author:bioerHits: 7681 times在分子克隆实验中,限制性内切酶是必不可少的工具酶。
无论是构建克隆载体还是表达载体,要根据载体选择合适的内切酶(当然,使用T 载就不必考虑了)。
先将引物设计好,然后添加酶切识别序列到引物5' 端。
常用的内切酶比如Bam HI、EcoRI、HindII I、NdeI、XhoI等可能你都已经记住了它们的识别序列,不过为了保险起见,还是得查证一下。
下面是一些常用的II型内切酶的识别序列,仅供参考。
先介绍一下什么是II型内切酶吧。
The Type II restri ction system s typica lly contai n indivi dualrestri ction enzyme s and modifi catio n enzyme s encode d by separa te genes. The Type II restri ction enzyme s typica lly recogn ize specif ic DNA sequen ces and cleave at consta nt positi ons at or closeto that sequen ce to produc e 5-phosph atesand 3-hydrox yls. Usuall y they requir e Mg 2+ ions as a cofact or, althou gh some have more exotic requir ement s. The methyl trans feras es usuall y recogn ize the same sequen ce althou gh some are more promis cuous. Threetypesof DNA methyl trans feras es have been foundas part of Type II R-M system s formin g either C5-methyl cytos ine, N4-methyl cytos ine or N6-methyl adeni ne.酶类型识别序列ApaIType II restri ctionenzyme5'GGGCC^C 3'BamHIType II restri ctionenzyme5' G^GATCC3'BglIIType II restri ctionenzyme5' A^GATCT3'EcoRIType II restri ctionenzyme5' G^AATTC3'HindII IType II restri ctionenzyme5' A^AGCTT3'KpnIType II restri ctionenzyme5' GGTAC^C 3'NcoIType II restri ctionenzyme5' C^CATGG3' NdeIType II restri ctionenzyme5' CA^TATG 3'NheIType II restri ctionenzyme5' G^CTAGC3'NotIType II restri ctionenzyme5' GC^GGCCGC 3'SacIType II restri ctionenzyme5' GAGCT^C 3'SalIType II restri ctionenzyme5' G^TCGAC3' SphIType II restri ctionenzyme5' GCATG^C 3'XbaIType II restri ctionenzyme5' T^CTAGA3'XhoIType II restri ctionenzyme5' C^TCGAG3'要查找更多内切酶的识别序列,你还可以选择下面几种方法:1. 查你所使用的内切酶的公司的目录或者网站;2. 用软件如:Primer Premie r5.0或Bioe dit等,这些软件均提供了内切酶识别序列的信息;3. 推荐到NEB的REBA SE数据库去查(网址:http://rebas/rebase/rebase.html)当你设计好引物,添加上了内切酶识别序列,下一步或许是添加保护碱基了,可以参考:NEB公司网站提供的关于设计PC R引物保护碱基参考表下载(也可见图片)双酶切buf fer的选择(MBI、罗氏、NEB、Prom eg a、Takara)再给大家推荐一种新的不需要连接反应的分子克隆方法,优点包括:①设计引物不必考虑选择什么酶切位点;②不必考虑保护碱基的问题;③不必每次都选择合适的酶来酶切质粒制备载体;④而且不需要D NA连接酶;⑤假阳性几率低(因为没有连接反应这一步,载体自连的问题没有了)。
基于多样性SAT求解器和新颖性搜索的软件产品线测试
基于多样性SAT求解器和新颖性搜索的软件产品线测试基于多样性SAT求解器和新颖性搜索的软件产品线测试一、引言随着软件产品线的发展和普及,软件的测试工作变得日益重要和复杂。
传统的软件测试方法在面对大规模和高度定制化的软件产品线时显得力不从心,因此需要寻找一种更加高效和灵活的测试方法。
本文将介绍基于多样性SAT求解器和新颖性搜索的软件产品线测试方法,该方法能够有效地解决软件产品线测试中的挑战。
二、背景和挑战软件产品线是指在一组具有共同特征和变体的软件系统中,通过共享和重用已有的软件资产来实现定制化的软件开发。
由于软件产品线的规模庞大和变体复杂,传统的软件测试方法往往面临以下挑战:1. 资源消耗:传统的测试方法需要耗费大量的时间和人力资源来设计和执行测试用例,对于规模庞大的软件产品线来说是不可行的。
2. 覆盖率问题:传统的测试方法往往只能覆盖到部分测试用例,无法完全覆盖软件产品线的所有变体,从而导致潜在的缺陷无法被发现。
3. 变体间关联性:软件产品线中的不同变体之间存在着相关性,测试某个变体时往往需要考虑其他变体的影响,传统的测试方法难以满足这一需求。
三、多样性SAT求解器多样性SAT求解器是一种基于约束求解的技术,能够在给定的约束条件下自动找出所有的可行解。
它的特点是能够生成多个不同的解,从而提供了在软件产品线测试中的多样性。
多样性SAT求解器的主要优势包括:1. 搜索空间广:多样性SAT求解器能够遍历整个搜索空间,找出不同的测试用例,从而提供了更高的覆盖率。
2. 快速响应:多样性SAT求解器采用高效的算法和数据结构,能够迅速找到解决方案,节省了测试时间。
3. 自适应性:多样性SAT求解器能够根据测试需求和目标进行自适应调整,提供个性化的测试解决方案。
四、新颖性搜索传统的软件测试方法往往采用随机生成测试用例的方式,难以找到具有新颖性的测试用例。
而新颖性搜索技术能够根据已有的测试用例和已知的变体信息,自动生成具有差异性和新颖性的测试用例。
数学建模专业词汇
算法常用术语中英对照算法常用术语中英对照Data Structures 基本数据结构Dictionaries 字典Priority Queues 堆Graph Data Structures 图Set Data Structures 集合Kd-Trees 线段树Numerical Problems 数值问题Solving Linear Equations 线性方程组Bandwidth Reduction 带宽压缩Matrix Multiplication 矩阵乘法Determinants and Permanents 行列式Constrained and Unconstrained Optimization 最值问题Linear Programming 线性规划Random Number Generation 随机数生成Factoring and Primality Testing 因子分解/质数判定Arbitrary Precision Arithmetic 高精度计算Knapsack Problem 背包问题Discrete Fourier Transform 离散Fourier变换Combinatorial Problems 组合问题Sorting 排序Searching 查找Median and Selection 中位数Generating Permutations 排列生成Generating Subsets 子集生成Generating Partitions 划分生成Generating Graphs 图的生成Calendrical Calculations 日期Job Scheduling 工程安排Satisfiability 可满足性Graph Problems -- polynomial 图论-多项式算法Connected Components 连通分支Topological Sorting 拓扑排序Minimum Spanning Tree 最小生成树Shortest Path 最短路径Transitive Closure and Reduction 传递闭包Matching 匹配Eulerian Cycle / Chinese Postman Euler回路/中国邮路Edge and Vertex Connectivity 割边/割点Network Flow 网络流Drawing Graphs Nicely 图的描绘Drawing Trees 树的描绘Planarity Detection and Embedding 平面性检测和嵌入Graph Problems -- hard 图论-NP问题Clique 最大团Independent Set 独立集Vertex Cover 点覆盖Traveling Salesman Problem 旅行商问题Hamiltonian Cycle Hamilton回路Graph Partition 图的划分Vertex Coloring 点染色Edge Coloring 边染色Graph Isomorphism 同构Steiner Tree Steiner树Feedback Edge/Vertex Set 最大无环子图Computational Geometry 计算几何Convex Hull 凸包Triangulation 三角剖分Voronoi Diagrams Voronoi图Nearest Neighbor Search 最近点对查询Range Search 范围查询Point Location 位置查询Intersection Detection 碰撞测试Bin Packing 装箱问题Medial-Axis Transformation 中轴变换Polygon Partitioning 多边形分割Simplifying Polygons 多边形化简Shape Similarity 相似多边形Motion Planning 运动规划Maintaining Line Arrangements 平面分割Minkowski Sum Minkowski和Set and String Problems 集合与串的问题Set Cover 集合覆盖Set Packing 集合配置String Matching 模式匹配Approximate String Matching 模糊匹配Text Compression 压缩Cryptography 密码Finite State Machine Minimization 有穷自动机简化Longest Common Substring 最长公共子串Shortest Common Superstring 最短公共父串robustness 鲁棒性rate of convergence 收敛速度数据结构方面数据结构基本英语词汇数据抽象 data abstraction数据元素 data element数据对象 data object数据项 data item数据类型 data type抽象数据类型 abstract data type逻辑结构 logical structure物理结构 phyical structure线性结构 linear structure非线性结构 nonlinear structure基本数据类型 atomic data type固定聚合数据类型 fixed-aggregate data type可变聚合数据类型 variable-aggregate data type 线性表 linear list栈 stack队列 queue串 string数组 array树 tree图 grabh查找,线索 searching更新 updating排序(分类) sorting插入 insertion删除 deletion前趋 predecessor后继 successor直接前趋 immediate predecessor直接后继 immediate successor双端列表 deque(double-ended queue)循环队列 cirular queue指针 pointer先进先出表(队列)first-in first-out list后进先出表(队列)last-in first-out list栈底 bottom栈定 top弹出 pop队头 front队尾 rear上溢 overflow下溢 underflow数组 array矩阵 matrix多维数组 multi-dimentional array以行为主的顺序分配 row major order以列为主的顺序分配 column major order 三角矩阵 truangular matrix对称矩阵 symmetric matrix稀疏矩阵 sparse matrix转置矩阵 transposed matrix链表 linked list线性链表 linear linked list单链表 single linked list多重链表 multilinked list循环链表 circular linked list双向链表 doubly linked list十字链表 orthogonal list广义表 generalized list链 link指针域 pointer field链域 link field头结点 head node头指针 head pointer尾指针 tail pointer串 string空白(空格)串 blank string空串(零串)null string子串 substring树 tree子树 subtree森林 forest根 root叶子 leaf结点 node深度 depth双亲 parents孩子 children兄弟 brother祖先 ancestor子孙 descentdant二叉树 binary tree平衡二叉树 banlanced binary tree 满二叉树 full binary tree完全二叉树 complete binary tree遍历二叉树 traversing binary tree 二叉排序树 binary sort tree二叉查找树 binary search tree线索二叉树 threaded binary tree哈夫曼树 Huffman tree有序数 ordered tree无序数 unordered tree判定树 decision tree双链树 doubly linked tree数字查找树 digital search tree树的遍历 traversal of tree先序遍历 preorder traversal中序遍历 inorder traversal后序遍历 postorder traversal图 graph子图 subgraph有向图 digraph(directed graph)无向图 undigraph(undirected graph) 完全图 complete graph连通图 connected graph非连通图 unconnected graph强连通图 strongly connected graph 弱连通图 weakly connected graph加权图 weighted graph有向无环图 directed acyclic graph 稀疏图 spares graph稠密图 dense graph重连通图 biconnected graph二部图 bipartite graph边 edge弧 arc路径 path回路(环)cycle弧头 head弧尾 tail源点 source终点 destination汇点 sink权 weight连接点 articulation point初始结点 initial node终端结点 terminal node相邻边 adjacent edge相邻顶点 adjacent vertex关联边 incident edge入度 indegree出度 outdegree最短路径 shortest path有序对 ordered pair无序对 unordered pair简单路径 simple path简单回路 simple cycle连通分量 connected component邻接矩阵 adjacency matrix邻接表 adjacency list邻接多重表 adjacency multilist遍历图 traversing graph生成树 spanning tree最小(代价)生成树 minimum(cost)spanning tree 生成森林 spanning forest拓扑排序 topological sort偏序 partical order拓扑有序 topological orderAOV网 activity on vertex networkAOE网 activity on edge network关键路径 critical path匹配 matching最大匹配 maximum matching增广路径 augmenting path增广路径图 augmenting path graph查找 searching线性查找(顺序查找)linear search (sequential search) 二分查找 binary search分块查找 block search散列查找 hash search平均查找长度 average search length散列表 hash table散列函数 hash funticion直接定址法 immediately allocating method数字分析法 digital analysis method平方取中法 mid-square method折叠法 folding method除法 division method随机数法 random number method排序 sort内部排序 internal sort外部排序 external sort插入排序 insertion sort随小增量排序 diminishing increment sort选择排序 selection sort堆排序 heap sort快速排序 quick sort归并排序 merge sort基数排序 radix sort外部排序 external sort平衡归并排序 balance merging sort二路平衡归并排序 balance two-way merging sort多步归并排序 ployphase merging sort置换选择排序 replacement selection sort文件 file主文件 master file顺序文件 sequential file索引文件 indexed file索引顺序文件 indexed sequential file索引非顺序文件 indexed non-sequential file直接存取文件 direct access file多重链表文件 multilist file倒排文件 inverted file目录结构 directory structure树型索引 tree index。
工具变量与两阶段最小二乘法
? In order for a variable, z, to serve as a valid instrument for x, the following must be true 针对内生变量 x 的一个有效的工具变量 z 应当满 足如下条件
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Long Jingfan1,2 (
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1.School of Mathematical Sciences, Beijing Normal University, Beijing 100875, China 2.Department of Computer Science, Information Technology Institute, Beijing 100101, China E-mail: fanggs@
m∈Z\{0}
λm |f (m)|2
.
K1 (x, y ) = K1,γ (x, y ) = 1 + γ
m∈Z\{0}
λm exp{2π im(x − y )} =
λ (γ, m)
λ is a reproducing kernel in the Hilbert space H1 , where 1, if m = 0, ρλ (γ, m) = γ −1 λ−1 , if m = 0. m
1
= f
1,γ
= f, f
1/2 1
= f, f
1/2 1,γ
λ It is clear that if f ∈ H1 , then f has absolutely convergent Fourier series: thus, f is continuous, and
= |f (0)|2 + γ −1
∗ Received
No.4
Fang & Long: TRACTABILITY OF MULTIVARIATE INTEGRATION PROBLEM
791
of applications, and large d is used in financial mathematics, where it is quite common to integrate functions of 360 or more variables. For path integration, d = ∞, and an accurate approximation of the path integral requires approximations for d-dimensional integrals, where d can be arbitrarily large (see [15]). If d is large then often some variables are “more important” than others. To model such a situation, weighted tensor product norms, where different norms are taken for different variables, are considered in Refs.[8, 12, 13]. The authors concentrate on quasi-Monte Carlo formulas, prove upper and lower bounds with respect to discrepancies, and give a partial answer to why quasi-Monte Carlo algorithms can work well for arbitrarily large d. It is done by identifying classes of functions for which the effect of the dimension d is negligible. Motivated by Hickernell and Wo´ zniakowski [8] and Sloan and Wo´ zniakowski [13], we study the tractability of multivariate integration over the unit cube in weighted periodic continuous classes of functions with absolutely convergent Fourier coefficients for the worst case setting. λ λ Our function class is the unit ball on a Hilbert space Hd = Hd,γ , which is a tensor product of d λ λ different Hilbert spaces H1 = H1,γ of 1-periodic complex-valued L2 functions f defined on [0,1] λ with absolutely convergent Fourier series. The inner products of H1 have the following form f, g
(2)
∞
η (λ) :=
m=1
λm < ∞,
(3)
which characterizes the rate of decay of the Fourier coefficients,
1
f (m) =
0
exp(−2π imx)f (x)dx,
m ∈ Z,
and γ > 0 is a weight that may take different values in different components of the tensor λ product. The norm of H1 is given by 1/2 f
(4)
792
ACTA MATHEMATICA SCIENTIA
Vol.27 Ser.B
Indeed, by equation (1), we find that f, K1 (·, y )
1
=
m∈Z\{0}
ρλ (γ, m)f (m)K (·, y ) ρλ (γ, m)f (m) exp(2π imy ) = f (y ). ρλ (γ, m)
1
Introduction
Multivariate problem is of increasing importance in computational mathematics [2, 5, 6, 8, 12, 15, 16]. Especially, it is indeed quite common to approximate integrals of functions of d variables. All values of d seem to be of practical interests, and small d occurs in a number
October 19, 2005; revised March 10, 2006. Project supported by the National Natural Science Foundation of China (10671019), Research Fund for the Doctoral Program Higher Education(20050027007), and Beijing Educational Committee (2002Kj112) Corresponding author: Fang Gensun
Acta Mathematica Scientia 2007,27B(4):790–802
TRACTABILITY OF MULTIVARIATE INTEGRATION PROBLEM FOR PERIODIC CONTINUOUS FUNCTIONS∗
Abstract The authors study the tractability and strong tractability of a multivariate integration problem in the worst case setting for weighted 1-periodic continuous functions spaces of d coordinates with absolutely convergent Fourier series. The authors reduce the initial error by a factor ε for functions from the unit ball of the weighted periodic continuous functions spaces. Tractability is the minimal number of function samples required to solve the problem in polynomial in ε−1 and d, and the strong tractability is the presence of only a polynomial dependence in ε−1 . This problem has been recently studied for quasi-Monte Carlo quadrature rules, quadrature rules with non-negative coefficients, and rules for which all quadrature weights are arbitrary for weighted Korobov spaces of smooth periodic functions of d variables. The authors show that the tractability and strong tractability of a multivariate integration problem in worst case setting hold for the weighted periodic continuous functions spaces with absolutely convergent Fourier series under the same assumptions as in Ref.[14] on the weights of the Korobov space for quasi-Monte Carlo rules and rules for which all quadrature weights are non-negative. The arguments are not constructive. Key words Information-based complexity, tractability, Monte Carlo methods, multivariate integration 2000 MR Subject Classification 41A55, 41A63, 65D30, 41A25