Monte Carlo Study of Correlations Near the Ground State of the Triangular Antiferromagnetic
MonteCarlo方法
2. 频率检验
检验每组观测频数 ni与理论频数mi = N 1/k之间相差的显著性
Monte Carlo 方法解决实际问题的过程中 , 主要有以下几个内容 ① 建立简单而又便于实现的概率统计模型 , 使所求的解正是该模型的某 一事件的概率或数学期望 , 或该模型能够直接描述实际的随机过程。
② 根据概率统计模型的特点和计算的需求 , 改进模型 , 以便减小方差和 减低费用 , 提高计算效率。
选择递推函数必须注意以下几点 :
① 随机性好 ; ② 在计算机上容易实现 ; ③ 省时 ; ④ 伪随机数的周期长。
2 伪随机数的产生方法
最基本的伪随机数是均匀分布的伪随机数。 该方法是首先给一个 2r位的数 , 取其中间的 r位数码作为第一个伪随机数 , 然后将这个数平方 , 构成一个新的 2 r位的数 , 再取中间的 r位数作为第 二个伪随机数。 如此循环可得到一个伪随机数序列。 该方法的递推公式为
提高精度一位数 , 抽样次数要增加100 倍 ; 减小随机变量的标准 差 , 可以减小误差 。
Monte Carlo 方法具有以下四个重要特征 :
① 由于 Monte Carlo 方法是通过大量简单的重复抽样来实现的 , 因 此 , 方法和程序的结构十分简单 。
② 收敛速度比较慢 , 因此 , 较适用于求解精度要求不高的问题。
具体试验步骤如下 :
(1) 产生服从给定分布函数 g(x)的随机变量值 xi (2)检查 xi是否落入积分区域(a≤ x ≤ b) , 如果满足条件 , 则记录一次。
高维Copula—MonteCarlo模型在投资组合中的应用研究
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( )在 [ ,]区 间 内 随 机 产 生 ”一 1 数 U , 2 … ,,1 将 2 o1 个 lU. “ , r 画 单 个 金 融 资 产 的 分 布 和 c p l 数 描述 多 个 金 融 资 产 间 ou a函 它代 人函数 G中, 则该 函数则是 关于 U 的一 元函数 。3 ( )由 的 相 依 结 构 。运 用 Mo t al 拟 方 法 计 算 金 融 变 量 资 neC r o模 cU ,2 … , 1z )的 非 负 性 可 知 , G是 关 于 U 调 递 ( l U , “ , 则 单 产 的 Va , 结 合 效 用 函 数 去 确 定 投 资 组 合 的 比例 系 数 , r并 从 增 的 , G( 在 U )的值 域[ , 1 ]内随机取一点 , 能反解 o G( ) 若 而 获 得 最 优 的资 金 分 配 方 案 。 出 U 一 G ) - ( ,则 ( l U ,3 … , 为 满 足 某 选 定 U ,2 U , U ) 1 Co ua研 究 现 状 pl Cpl o ua函数 的 一 点 。 此 循 环 计 算 , 以 产 生 一 组 由 某 个 如 可 C p l 理 论 研 究 源 于 S l , Nesn比 较 系 统 地 介 绍 o ua ka 而 r le C p l 数 决 定 的 随 机 序 列 {Uj uj uj … ,n) 一 1 o ua函 ( l,2 ,a , u , j , 了 C pl o ua的定 义 、 建 方 法 、 c i da o u 构 Arhme enC p l 变 量 间 2, } a及 … 。
蒙特卡罗方法教材
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例1. 蒲丰投针问题
数学建模专题之 Monte Carlo方法
利用关系式:
2l p d
求出π值
2l 2l 1 2l N ( ) dp d p d n
其中N 为投计次数,n 为针与平行线相交 次数。这就是古典概率论中著名的蒲丰氏问题。
分析:这是一个概率问题,可以通过理论计算得到相应的概 率和期望值.但这样只能给出作战行动的最终静态结果,而显 示不出作战行动的动态过程. 为了能显示我方20次射击的过程,现采用模拟的方式。
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问题分析
需要模拟出以下rlo方法
[1] 观察所对目标的指示正确与否
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Matlab中的取整函数
fix(x) floor(x) ceil(x) round(x)
数学建模专题之 Monte Carlo方法
: 截尾取整,直接将小数部分舍去 : 不超过 x 的最大整数 : 不小于 x 的最小整数
: 四舍五入取整
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小实例一:投掷硬币
数学建模专题之 Monte Carlo方法
模拟结果
试验 序号 1 2 3 4 5 6 7 8 9 10 投硬币 结 果 正 正 反 正 正 反 正 正 反 反 ∨ ∨ ∨ ∨ ∨ ∨ ∨ 3 6 ∨ ∨ 指示 正确 ∨ ∨ ∨ 1 2 指 示 不正确 掷骰子 结 果 4 4 ∨ ∨ ∨ ∨ ∨ 0 1 ∨ ∨
数学建模专题之 Monte Carlo方法
John Von Neumann (1903-1957)
蒙特卡罗方法 (Monte Carlo simulation)教材
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Monte Carlo模拟
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2.Monte Carlo方法简史
Stanislaw Ulam (1909-1984)
S. Ulam is credited as the inventor of Monte Carlo method in 1940s, which solves mathematical problems using statistical sampling.
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Monte Carlo模拟
第一章 引言 (Introduction)
1. 2. 3. 4. Monte Monte Monte Monte Carlo方法 Carlo方法简史 Carlo模拟的应用 Carlo算法的主要组成部分
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1.Monte Carlo方法
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2.Monte Carlo方法简史 Buffon投针实验
1768年,法国数学家Comte de Buffon利用投针实验估 计的值
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2.Monte Carlo方法简史
Problem of Buffon’s needle: If a needle of length l is dropped at random on the middle of a horizontal surface ruled with parallel lines a distance d > l apart, what is the probability that the needle will cross one of the lines?
蒙特卡罗方法 (Monte Carlo simulation)
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• The random vector is uniformly distributed on the region [0,d)×[0,). Accordingly, it has probability density function 1/d. • The probability that the needle will cross one of the lines is given by the integral
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Monte Carlo模拟
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4.Monte Carlo算法的主要组成部分
Monte Carlo算法的主要组成部分 概率密度函数(pdf) 必须给出描述一个物理系统的一组概率密度函数;
随机数产生器 能够产生在区间[0,1]上均匀分布的随机数 抽样规则 如何从在区间[0,1]上均匀分布的随机数出发,随机抽 取服从给定的pdf的随机变量;
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2.Monte Carlo方法简史 Enrico Fermi
• 1930年,利用Monte Carlo方法研究中子的扩散 • 并设计了一个Monte Carlo机械装置,Fermiac,用于计算核 反应堆的临界状态
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Monte Carlo模拟
第一章 引言 (Introduction)
1. 2. 3. 4. Monte Monte Monte Monte Carlo方法 Carlo方法简史 Carlo模拟的应用 Carlo算法的主要组成部分
MonteCarlo模拟教程
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1901 3408
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蒙特卡罗投点法是蒲丰投针实验的推广:
在一个边长为a的正方形内随机投点,
该点落在此正方形的内切圆中的概率 y
(a/2,a/2)
应为该内切圆与正方形的面积比值,
即 πa/22 : a2 π/4
n=10000; a=2; m=0; for i=1:n
ox
x=rand(1)*a; y=rand(1)*a;
举例
例1 在我方某前沿防守地域,敌人以一个炮排(含两门火炮) 为单位对我方进行干扰和破坏.为躲避我方打击,敌方对其阵地 进行了伪装并经常变换射击地点.
经过长期观察发现,我方指挥所对敌方目标的指示有50%是准 确的,而我方火力单位,在指示正确时,有1/3的射击效果能毁 伤敌人一门火炮,有1/6的射击效果能全部毁伤敌人火炮.
Monte Carlo 模拟
内容提纲
➢1.引言 ➢2.Monte Carlo模拟基本思想 ➢3.随机数生成函数 ➢4.应用实例举例 ➢5.排队论模拟 ➢6.Monte Carlo模拟求解规划问题
Monte Carlo方法:
引言(Introduction)
蒙特卡罗方法,又称随机模拟方法,属于计算数学的一个分支,它是在上世纪四 十年代中期为了适应当时原子能事业的发展而发展起来的。亦称统计模拟方法, statistical simulation method 利用随机数进行数值模拟的方法
MonteCarlo蒙特卡洛法简介.ppt
实现从已知概率分布抽样
构造了概率模型以后, 按照这个概率分 布抽取随机变量 (或随机向量),这一 般可以直接由软件包调用,或抽取均匀 分布的随机数构造。这样,就成为实现 蒙特卡罗方法模拟实验的基本手段,这 也是蒙特卡罗方法被称为随机抽样的原 因。
建立各种估计量
一般说来,构造了概率模型并能从中抽 样后,即实现模拟实验后,我们就要确 定一个随机变量,作为所要求的问题的 解,我们称它为无偏估计。建立各种估 计量,相当于对模拟实验的结果进行考 察和登记,从中得到问题的解。
例子
考虑平面上的一个边长为1的正方形及其 内部的一个形状不规则的“图形”,如 何求出这个“图形”的面积呢?Monte Carlo方法是这样一种“随机化”的方法: 向该正方形“随机地”投掷N个点落于 “图形”内,则该“图形”的面积近似 为M/N。
比喻
可用民意测验来作一个不严格的比喻。 民意测验的人不是征询每一个登记选民 的意见,而是通过对选民进行小规模的 抽样调查来确定可能的民意。其基本思 想是一样的。
基本思想和原理
基本思想:当所要求解的问题是某种事件出现 的概率,或者是某个随机变量的期望值时,它 们可以通过某种“试验”的方法,得到这种事 件出现的频率,或者这个随机变数的平均值, 并用它们作为问题的解。
原理:抓住事物运动的几何数量和几何特征, 利用数学方法来加以模拟,即进行一种数字模 拟实验。
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Monte Carlo 模拟连续过程的欧式 期权定价-
.-0.4326 0.2877 -1.6656 -1.1465 0.1253 1.1909
精确性
由于Monte Carlo 方法的随机性,精确性 建立在大量的重复模拟上,最后去平均 值。
decay of correlation 数学名词
decay of correlation 数学名词Decay of correlation(相关性的衰减)refers to the decrease in correlation between two variables as the distance between them increases. It is a mathematical concept used to quantify the relationship between two variables across different spatial or temporal distances.1. The decay of correlation between rainfall and crop yield was observed as the distance between the two fields increased.雨量与农作物产量之间的相关性随着两个田地之间的距离增加而减弱。
2. The study analyzed the decay of correlation between interest rates and stock market performance over a one-year timespan.该研究分析了利率和股市表现之间的相关性在一年的时间内是如何衰减的。
3. As the distance between two cities increased, thedecay of correlation between their population sizes became more noticeable.随着两个城市之间的距离增加,它们的人口规模之间的相关性衰减变得更加明显。
4. The researchers used statistical methods to determine the decay of correlation between air pollution andrespiratory diseases in different neighborhoods.研究人员使用统计方法来确定不同社区之间空气污染和呼吸道疾病之间的相关性衰减。
蒙特卡罗模拟法对投资组合风险价值估计的实证研究
蒙特卡罗模拟法对投资组合风险价值估计的实证研究【摘要】蒙特卡罗(monte carlo)方法的实质是通过大量随机试验,利用概率论解决问题的一种数值方法,基本思想是基于概率和体积间的相似性。
这种方法在风险价值的衡量上有着不可替代的作用,本文对这种方法的概念形式进行简要说明,并且主要对运用这种方法对投资组合风险价值进行估计的程序进行了设计并通过实例进行了验证。
【关键词】蒙特卡罗;风险价值;估计;实现一、蒙特卡罗monte carlo的概念、形式与一般步骤蒙特卡罗(monte carlo)方法,又称随机抽样或统计试验方法,以概率和统计理论方法为基础的一种计算方法。
将所求解的问题同一定的概率模型相联系,用电子计算机实现统计模拟或抽样,以获得问题的近似解。
为象征性地表明这一方法的概率统计特征,故借用赌城蒙特卡罗命名。
又称统计模拟法、随机抽样技术。
由s.m.乌拉姆和j.冯·诺伊曼在20世纪40年代为研制核武器而首先提出。
它的基本思想是,首先建立一个概率模型或随机过程,使它们的参数,如概率分布或数学期望等问题的解;然后通过对模型或过程的观察或抽样试验来计算所求参数的统计特征,并用算术平均值作为所求解的近似值。
(一)积分形式做monte carlo时,求解积分的一般形式是:x为自变量,它应该是随机的,定义域为(x0, x1),f(x)为被积函数,ψ(x)是x的概率密度。
在计算欧式期权例子中,x为期权到期日股票价格,由于我们计算期权价格的时候该期权还没有到期,所以此时x是不确定的(是一随机变量),我们按照相应的理论,假设x的概率密度为ψ(x)、最高可能股价为x1(可以是正无穷)、最低可能股价为x0(可以是0),另外,期权收益是到期日股票价格x和期权行权价格的函数,我们用f(x)来表示期权收益。
(二)一般步骤我将monte carlo分为三加一个步骤:1、依据概率分布ψ(x)不断生成随机数x,并计算f(x)由于随机数性质,每次生成的x的值都是不确定的,为区分起见,我们可以给生成的x赋予下标。
蒙特卡洛方法2
由大数定律可知,当 n ,样本的均值趋 n 逐渐增大 向与理论分布的期望,因此利用样本容量 n 这一趋势来模拟 这一趋势,在这种趋势下, 应该呈现出越来 样本的均值与理论分布期望的误差 越小的趋势
一些人进行了实验,其结果列于下表 :
实验者 沃尔弗(Wolf) 年份 1850 投计次数 5000 π的实验值 3.1596
斯密思(Smith)
福克斯(Fox) 拉查里尼 (Lazzarini)
1855
1894 1901
3204
1120 3408
3.1553
3.1419 3.1415929
例2. 射击问题(打靶游戏)
MANIAC the Computer and the Man
Seated is Nick Metropolis, the background is the MANIAC vacuum tube computer
Monte Carlo
Direct Monte Carlo Important sampling Monte Carlo Metropolis Monte Carlo Dynamic Monte Carlo
因此,可以通俗地说,蒙特卡罗方法是用随机试 验的方法计算积分,即将所要计算的积分看作服从某 种分布密度函数f(r)的随机变量g(r)的数学期望
g g (r ) f (r )dr
0
通过某种试验,得到N个观察值r1,r2,…,rN(用概 率语言来说,从分布密度函数 f(r) 中抽取 N 个子样 r1 , r2 , … , rN ,),将相应的 N 个随机变量的值 g(r1) , g(r2),…,g(rN)的算术平均值
Quasi-Monte Carlo Techniques and Rare Event Sampling
Hence, the tail of the sum of iid random variables behaves asymptotically like the tail of maximum of the summands. Important elements of S are • the Lognormal distribution: Y = eX , where X is Gaussian, 2
• Weibull-type distributions: F ∼ cu1+γ −β e−x , 0 < β < 1, • Regular varying distributions (including Pareto): F (u) = where for all t > 0 :
(tu) limu→∞ L L(u)
This work was supported by the Austrian Science Foundation, projects S-8307MAT, S-8308-MAT and P-18392. Email addresses: juergen.hartinger@oeaw.ac.at (J¨ urgen Hartinger), kortscha@sbox.tugraz.at (Dominik Kortschak).
Dominik Kortschak
Institut f¨ ur Mathematik A, Technische Universit¨ at Graz, Steyrergasse 30/II, A-8010 Graz, Austria
蒙特卡罗模拟
第八章Monte Carlo法§ 8.1 概述Monte Carlo法不同于前面几章所介绍的确定性数值方法,它是用来解决数学和物理问题的非确定性的(概率统计的或随机的)数值方法。
Monte Carlo方法(MCM),也称为统计试验方法,是理论物理学两大主要学科的合并:即随机过程的概率统计理论(用于处理布朗运动或随机游动实验)和位势理论,主要是研究均匀介质的稳定状态[1]。
它是用一系列随机数来近似解决问题的一种方法,是通过寻找一个概率统计的相似体并用实验取样过程来获得该相似体的近似解的处理数学问题的一种手段。
运用该近似方法所获得的问题的解in spirit更接近于物理实验结果,而不是经典数值计算结果。
普遍认为我们当前所应用的MC技术,其发展约可追溯至1944年,尽管在早些时候仍有许多未解决的实例。
MCM的发展归功于核武器早期工作期间Los Alamos (美国国家实验室中子散射研究中心)的一批科学家。
Los Alamos小组的基础工作刺激了一次巨大的学科文化的迸发,并鼓励了MCM在各种问题中的应用[2]-[4]。
“Monte Carlo ”的名称取自于Monaco (摩纳哥)内以赌博娱乐而闻名的一座城市。
Monte Carlo方法的应用有两种途径:仿真和取样。
仿真是指提供实际随机现象的数学上的模仿的方法。
一个典型的例子就是对中子进入反应堆屏障的运动进行仿真,用随机游动来模仿中子的锯齿形路径。
取样是指通过研究少量的随机的子集来演绎大量元素的特性的方法。
例如,f (x)在a :::x :::b上的平均值可以通过间歇性随机选取的有限个数的点的平均值来进行估计。
这就是数值积分的Monte Carlo方法。
MCM已被成功地用于求解微分方程和积分方程,求解本征值,矩阵转置,以及尤其用于计算多重积分。
任何本质上属随机组员的过程或系统的仿真都需要一种产生或获得随机数的方法。
这种仿真的例子在中子随机碰撞,数值统计,队列模型,战略游戏,以及其它竞赛活动中都会出现。
概率统计中的MonteCarlo方法及其建模应用PPT课件
南京信息工程大学
2020/1/11 17:32
复合抽样方法
复合抽样方法的基本思想是由kahn提出的。
考虑如下复合分布:
f (x) f2(x | y)dF1(y)
其中f2(x|y)为给定Y=y时X的条件密度,F1(y)为Y的分布函数 如果X密度函数f(x)难于抽样,而X关于Y的条件密度函数 f2(x|y)以及Y的分布F1(y)均易于抽样,则X的随机数抽样:
i=1
i=1
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I-1
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p1
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为了实现由任意离散型分布的随机抽样,直接抽样方法 是非常理想的!
南京信息工程大学
2020/1/11 17:32
[1]离散型分布
例1.
掷骰子点数的抽样
P( X
1 I ) pi 6
按照离散分布的直接抽样:
(1)由U(0,1)抽取u
I -1
I
(2) x I , 当 pi u pi
i =1
i =1
即:
I 1 u I , I {1,2,3,4,5,6}, x I
6
6
等价于:I 1 6u I, I 1,2,3,4,5,6, x I
收敛速度与问题维数无关
– Monte Carlo方法的收敛速度为O(n -1/2),与一般数值方法相比很慢。 因此,用Monte Carlo方法不能解决精确度要求很高的问题
– Monte Carlo方法误差只与标准差和样本容量n有关,而与样本所 在空间无关,即Monte Carlo方法的收敛速度与问题维数无关,而 其他数值方法则不然。
蒙特卡罗方法MonteCarlosimulation
第六章 引言(Introduction)
Monte Carlo模拟在物理研究中的作用
第六章 引言(Introduction)
Monte Carlo模拟的步骤: 1. 根据欲研究的物理系统的性质,建立能够描述该系统特性 的理论模型,导出该模型的某些特征量的概率密度函数; 2. 从概率密度函数出发进行随机抽样,得到特征量的一些模 拟结果; 3. 对模拟结果进行分析总结,预言物理系统的某些特性。
第六章 引言(Introduction)
Monte Carlo方法简史 简单地介绍一下Monte Carlo方法的发展历史
1、Buffon投针实验: 1768年,法国数学家Comte de Buffon利用投针实验估计的值
L
d
p
2L d
第六章 引言(Introduction)
Problem of Buffon’s needle: If a needle of length l is dropped at random on the middle of a horizontal surface ruled with parallel lines a distance d>l apart, what is the probability that the needle will cross one of the lines?
第六章 引言(Introduction)
Solution:
The positioning of the needle relative to nearby lines can be described with a random vector which
[0, )
The random vector is uniformly distributed on the region [0,d)×[0,). Accordingly, it has probability density function 1/d.
Monte-Carlo法随机分析中提高样本有效性的尝试
,, … , 足 区间 [ m 一1 的均 匀 分 布 。对 该 数 2厶, 满 0, ] 列 进行适 当变换便得 到任 意 已知分 布 的随机数列 。
稍磐 O O O 幡婪
抽取 样本 的随机 性 , 时也 说 明 即使 是 抽 取 1 个 同 万 样本 , 不能保 证每 次抽 取样 本 的均值 和 标 准差 精 仍 确 满足 输 入 随 机 量 的 要 求 。而 此 时 若 假 设 单 次 M neC r 法 的 运算 时 问为 1 , 1万 次样 本 所 ot—al o 0S则 需 的运 算 时间为 2 .8h 计算成 本过 高 。但实 际工 77 , 程结构 的结 构复 杂 , 单次 运算 时间往 往超 过 1 , 0S且 实 际工程结 构 的失效 概 率 较 小 , 估 算 公 式得 到 的 按
通过理论分 析和数值算例验证 了该做法 的有效性。
关键词
Mot—al neC r o法 T 3 14 U 1. ;
随机分析
样本有效性 A
中图法分类号
文献标 志码
在土 木工 程 结构 的 随机 分 析 中 , n .al Mot Cr e o法 是 统计 型方 法 的 典 型 方 法 … 。该 法 对 问题 的 维 数 不 敏感 、 受任 何假 设 的约 束 以及 其 解 答 为 相 对 精 不 确解 , 从而 具有 很 强 的 适 用性 和 解 决 问题 能力 。但 Mot.al法 需要进 行 大 量 的确 定 性 分 析 , 算 工 neCr o 计 作 量 大 。 因 此 许 多 学 者 提 出 了 各 种 提 高 M ne ot.
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基于近端算子PHMC的机载雷达高度表参数估计
第 22 卷 第 2 期2024 年 2 月太赫兹科学与电子信息学报Journal of Terahertz Science and Electronic Information TechnologyVol.22,No.2Feb.,2024基于近端算子PHMC的机载雷达高度表参数估计郭牧欣,江舸*,黄博,经文(中国工程物理研究院电子工程研究所,四川绵阳621999)摘要:传统雷达高度表参数估计算法在面对参数的高维特性时会出现过拟合情况,导致参数估计精确度降低。
为此,提出一种新颖的基于近端算子修正的哈密顿蒙特卡洛(PHMC)算法,通过统计学的手段估计高程参数。
首先假设高程参数具有稀疏特性,并使用拉普拉斯分布对其进行表征,这种稀疏先验可表征高程突变的地形场景。
稀疏先验与似然函数之间为非共轭关系,使用分层贝叶斯的方法获得后验分布函数的闭合解,采用哈密顿蒙特卡洛(HMC)方法通过采样的方式解决贝叶斯推论中的参数估计问题,引入近端算子提供次梯度完成参数估计。
仿真数据验证了所提PHMC算法优于其他传统算法。
关键词:雷达高度表;哈密顿蒙特卡洛方法;分层贝叶斯;近端算子中图分类号:TN914.42 文献标志码:A doi:10.11805/TKYDA2022023Elevation parameter estimation for radar altimetry usingProximal Hamiltonian Monte CarloGUO Muxin,JIANG Ge*,HUANG Bo,JING Wen,(Institute of Electronics Engineering,China Academy of Engineering Physics,Mianyang Sichuan 621999,China)AbstractAbstract::Conventional radar altimetry parameter estimation algorithms often suffer from overfitting due to the high dimensionality of the parameters to be estimated. To this end, a novel ProximalHamiltonian Monte Carlo(PHMC) algorithm is proposed to estimate the elevation parameters in astatistical way. More specifically, Laplace distribution is employed to characterize the sparse prior toachieve the confidence estimation for the elevation parameters. This prior can depict the terrain sceneswith abrupt elevation changes. However, due to the non-conjugation between the sparse prior andGaussian likelihood function, the hierarchical Bayesian is employed to obtain the closed-form solution ofposterior distribution function. To overcome the difficulty of the Bayesian inference of high-dimensionalposterior, the Hamiltonian Monte Carlo(HMC) is utilized to solve the parameter estimation problem infully Bayesian inference. Since the potential energy obtained by posterior distribution does not satisfythe differentiable requirement of HMC, the proximal operator is applied to provide the sub-gradient toestimate parameters. Comparisons with the results using synthesis and practical data have demonstratedthe superiority of the proposed PHMC over other conventional algorithms.KeywordsKeywords::radar altimeter;Hamiltonian Monte Carlo;hierarchical Bayesian;proximal operator 雷达高度表作为一种重要的微波遥感仪器,可通过发射脉冲和记录回波测量平台的高度,可搭载于不同运动平台实现不同场景下的测量需求[1]。
Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM
Quasi-Monte Carlo Sampling toimprove the Efficiency of Monte Carlo EMWolfgang JankDepartment of Decision and Information TechnologiesUniversity of MarylandCollege Park,MD20742-1815wjank@November17,2003AbstractIn this paper we investigate an efficient implementation of the Monte Carlo EM al-gorithm based on Quasi-Monte Carlo sampling.The Monte Carlo EM algorithm is astochastic version of the deterministic EM(Expectation-Maximization)algorithm inwhich an intractable E-step is replaced by a Monte Carlo approximation.Quasi-MonteCarlo methods produce deterministic sequences of points that can significantly improvethe accuracy of Monte Carlo approximations over purely random sampling.One draw-back to deterministic Quasi-Monte Carlo methods is that it is generally difficult todetermine the magnitude of the approximation error.However,in order to implementthe Monte Carlo EM algorithm in an automated way,the ability to measure this erroris fundamental.Recent developments of randomized Quasi-Monte Carlo methods canovercome this drawback.We investigate the implementation of an automated,data-driven Monte Carlo EM algorithm based on randomized Quasi-Monte Carlo methods.We apply this algorithm to a geostatistical model of online purchases andfind that itcan significantly decrease the total simulation effort,thus showing great potential forimproving upon the efficiency of the classical Monte Carlo EM algorithm.Key words and phrases:Monte Carlo error;low-discrepancy sequence;Halton sequence;EM algo-rithm;geostatistical model.1IntroductionThe Expectation-Maximization(EM)algorithm(Dempster et al.,1977)is a popular tool in statis-tics and many otherfields.One limitation to the use of EM is,however,that quite often the E-step of the algorithm involves an analytically intractable,sometimes high dimensional integral. Hobert(2000),for example,considers a model for which the E-step involves intractable integrals of dimension twenty.The Monte Carlo EM(MCEM)algorithm,proposed by Wei&Tanner(1990), estimates this intractable integral with an empirical average based on simulated data.Typically,the simulated data is obtained by producing random draws from the distribution commanded by EM. By the law of large numbers,this integral-estimate can be made arbitrarily accurate by increasing the size of the simulated data.The MCEM algorithm typically requires a very high accuracy, especially at the later iterations.Booth&Hobert(1999),for example,report sample sizes of over 66,000at convergence.This suggests that the overall efficiency of MCEM could be improved by using simulation methods that achieve a high accuracy in the integral-estimate with smaller sample sizes.Recent research has provided evidence that entirely random draws do not necessarily result in the most efficient use of the simulated data.In particular,one criticism of random draws is that they often do not explore the sample space well(Morokoff&Caflisch,1995;Caflisch et al.,1997). For instance,points drawn at random tend to form clusters which leads to gaps where the sample space is not explored at all(see Figure1for illustration).This criticism has lead to the development of a variety of deterministic methods that provide for a better spread of the sample points.These deterministic methods are often classified as Quasi-Monte Carlo(QMC)methods.Theoretical as well as empirical research has shown that QMC methods can significantly increase the accuracy of the integral-estimate over random draws.Figure1about hereIn this paper we investigate an implementation of the MCEM algorithm based on QMC methods. Wei&Tanner(1990)point out that for an efficient implementation,the size of the simulated data should be chosen small at the initial stage but increased successively as the algorithm moves along. Early versions of the method require a manual,user-determined increase of the sample size,for instance,by allocating the amount of data to be simulated in each iteration already before the startof the algorithm(e.g.McCulloch,1997).Implementations of MCEM that determine the necessary sample size in an automated,data-driven fashion have been developed only recently(see Booth &Hobert,1999;Levine&Casella,2001;Levine&Fan,2003).Automated implementations of MCEM base the decision to increase the sample size on the magnitude of the error in the integral-approximation.In their seminal work,Booth&Hobert(1999)use statistical methods to estimate this error when the simulated data is generated at random.However,since QMC methods are deterministic in nature,statistical methods do not apply.Moreover,determining the error of the QMC integral-estimate analytically can be extremely hard(Caflisch et al.,1997).Recently,the development of randomized QMC methods has overcome this early drawback. Randomized Quasi-Monte Carlo(RQMC)methods combine the benefits of deterministic sampling methods,which achieve a more uniform exploration of the sample space,with the statistical advan-tages of random draws.A survey of recent advances in RQMC methods can be found in L’Ecuyer& Lemieux(2002).In this work we implement an automated MCEM algorithm based on RQMC meth-ods.Specifically,we demonstrate how to obtain a QMC sample from the distribution commanded by EM and we use the ideas of RQMC sampling to measure the error of the integral-estimate in every iteration of the algorithm.We implement this Quasi-Monte Carlo EM(QMCEM)algorithm within the framework of the automated MCEM formulation proposed by Booth&Hobert(1999).The remainder of this paper is organized as follows.In Section2we briefly motivate the ideas surrounding QMC and RQMC.In Section3we explain how RQMC methods can be used to imple-ment QMCEM in an automated,data-driven fashion.We apply this algorithm to a geostatistical model of online purchases in Section4and conclude withfinal remarks in Section5.2Quasi-Monte Carlo SamplingQuasi-Monte Carlo methods can be regarded as a deterministic counterpart to classical Monte Carlo.Suppose we want to evaluate an(analytically intractable)integralf(x)d x(1)I=C dover the d-dimensional unit cube,C d:=[0,1]d.Classical Monte Carlo integration randomly selects points x k∼Uniform(C d),k=1,...,m,and approximates(1)by the empirical average˜I=1mmk=1f(x k).(2)Quasi-Monte Carlo methods,on the other hand,select the points deterministically.Specifically, QMC methods produce a deterministic sequence of points that provides the best-possible spread in C d.These deterministic sequences are often referred to as low-discrepancy sequences(see,for example,Niederreiter,1992;Fang&Wang,1994).A variety of different low-discrepancy sequences exist.Examples include the Halton sequence (Halton,1960),the Sobol sequence(Sobol,1967),the Faure sequence(Faure,1982),and the Nieder-reiter sequence(Niederreiter,1992),but this list is not exhaustive.In this work we focus our attention on the Halton sequence since it is conceptually very appealing.2.1Halton SequencesLet b be a prime number.Then any integer k,k≥0,can be written in base-b representation ask=d j b j+d j−1b j−1+···+d1b+d0,where d i∈{0,1,...,b−1}for i=0,1,...,j.Define the base-b radical inverse function,φb(k),asφb(k)=d0b+d1b+...+d jb.Notice that for every integer k≥0,φb(k)∈[0,1].The k th element of the Halton sequence is obtained via the radical inverse function evaluated at k.Specifically,if b1,...,b d are d different prime numbers,then a d-dimensional Halton sequence of length m is given by{x1,...,x m},where the k th element of the sequence isx k=[φb1(k−1),...,φbd(k−1)]T,k=1,...,m.(3)(See Halton(1960)or Wang&Hickernell(2000)for more details.)Notice that the Halton sequence does not need to be started at the origin.Indeed,for any d-vector of non-negative integers,n=(n1,...,n d)T,say,the Halton sequence with thefirst elements skipped,x k=[φb1(n1+k−1),...,φbd(n d+k−1)]T,k=1,...,m,(4)remains a low-discrepancy sequence(see Pag`e s,1992;Bouleau&L´e pingle,1994).We will refer to the sequence defined by(4)as a Halton sequence with starting point n.Figure1shows thefirst 2500elements of a two-dimensional Haltion sequence with n=(0,0)T.2.2Randomized Quasi-Monte CarloOwen(1998b)points out that the main(practical)disadvantage of QMC is that determining the accuracy of the integral-estimate in(2)is typically very complicated,if not impossible.Moreover, since QMC methods are based on deterministic sequences,statistical procedures for error estimation do not apply.This drawback has lead to the development of randomized Quasi-Monte Carlo (RQMC)methods.L’Ecuyer&Lemieux(2002)suggest that any RQMC sequence should have the following two properties:1)every element of the sequence has a uniform distribution over C d;2)the low-discrepancy property of the sequence is preserved under the randomization.Thefirst property guarantees that the approximation˜I in(2)is an unbiased estimate of the integral in(1).Moreover, one can estimate its variance by generating r independent copies of˜I(which is typically done by generating r independent sequences x(j)1,...,x(j)m,j=1,...,r).Given a desired total simulation amount N=rm,smaller values of r(paired with a larger value of m)should result in a better accuracy of the integral-estimate,since it takes better advantage of the low-discrepancy property of each sequence.At the extreme,taking r=N and m=1simply reproduces classical Monte Carlo estimation.2.3Randomized Halton SequencesRecall that,regardless of the starting point,the Halton sequence remains a low-discrepancy se-quence.Wang&Hickernell(2000)use this fact to show that if the Halton sequence is started at a random point,x1∼Uniform(C d),then it satisfies the RQMC properties1)and2)from Subsec-tion2.2.In the following sections,we will use RQMC sampling based on the randomized Halton sequence.3Quasi-Monte Carlo EMThe Expectation-Maximization(EM)algorithm(Dempster et al.,1977)is an iterative procedure useful to approximate the maximum likelihood estimator(MLE)in incomplete data problems.Let y be a vector of observed data,let u be a vector of unobserved data or random effects and letθdenote a vector of parameters.Furthermore,let f(y,u;θ)denote the joint density of the completedata,(y,u).Let L(θ;y)=f(y,u;θ)d u denote the(marginal)likelihood function for this model.The MLE,ˆθ,maximizes L(·;y).In each iteration,the EM algorithm performs an expectation and a maximization step.Let θ(t−1)denote the current parameter value.Then,in the t th iteration of the algorithm,the E-step computes the conditional expectation of the complete data log-likelihood,conditional on the observed data and the current parameter value,Q(θ|θ(t−1))=Elog f(y,u;θ)|y;θ(t−1).(5)The t th EM update,θ(t),maximizes(5).That isθ(t)satisfiesQ(θ(t)|θ(t−1))≥Q(θ|θ(t−1))(6)for allθin the parameter space.This is also known as the M-step.The M-step is often implemented using standard numerical methods like Newton-Raphson(see Lange,1995).Solutions to overcome a difficult M-step have been proposed in,for example,Meng&Rubin(1993).Given an initial value θ(0),the EM algorithm produces a sequence{θ(0),θ(1),θ(2),...}that,under regularity conditions (see Boyles,1983;Wu,1983),converges toˆθ.In this work we focus on the situation when the E-step does not have a closed form solution. Wei&Tanner(1990)proposed to approximate an analytically intractable expectation in(5)by the empirical average˜Q(θ|θ(t−1))≡˜Q(θ|θ(t−1);u1,...,u m t)=1m tm tk=1log f(y,u k;θ),(7)where u1,...,u mtare simulated from the conditional distribution f(u|y;θ(t−1)).Then,by the law of large numbers,˜Q(θ|θ(t−1))will be a reasonable approximation to Q(θ|θ(t−1))if m t is large enough.We consider a modification of(7)suitable for RQMC sampling.Let u(j)1,...,u(j)mt,j=1,...,r, be r independent RQMC sequences of length m t,each simulated from f(u|y;θ(t−1)).(The details of how to simulate a RQMC sequence from f(u|y;θ(t−1))are deferred until Subsection3.2.)Then, an unbiased estimate of(5)is given by the pooled estimate˜Q P(θ|θ(t−1))=1rrj=1˜Q(j)(θ|θ(t−1)),(8)where˜Q(j)(θ|θ(t−1))=˜Q(θ|θ(t−1);u(j)1,...,u(j)mt)in(7).The t th Quasi-Monte Carlo EM(QMCEM) update,˜θ(t),maximizes˜Q P(·|θ(t−1)).3.1Increasing the length of the RQMC sequencesWe have pointed out earlier that the Monte Carlo sample sizes m t should be increased successively as the algorithm moves along.In fact,Booth et al.(2001)argue that MCEM will never converge if m t is heldfixed across iterations because of a persevering Monte Carlo error(see also Chan&Ledolter, 1995).While earlier versions of the method choose the Monte Carlo sample sizes in a deterministic fashion before the start of the algorithm(e.g.McCulloch,1997),the same deterministic allocation of Monte Carlo resources that works well in one problem may result in a very inefficient(or inaccurate) algorithm in another problem.Thus,data-dependent(and user-independent)sample size rules are necessary in order to implement MCEM in an automated way.Booth&Hobert(1999)base the decision of a sample size increase on the noise in the parameter updates(see also Levine&Casella, 2001;Levine&Fan,2003).Let˜θ(t−1)denote the current QMCEM parameter value and let˜θ(t)denote the maximizer of˜Q P(·|˜θ(t−1))in(8)based on r independent RQMC sequences each of length mt.Thus,˜θ(t)satisfies˜F P(˜θ(t)|˜θ(t−1))=0,(9) where we define˜F P(θ|θ )=∂˜Q P(θ|θ )/∂θ.Letθ(t)denote the parameter update of the determin-istic EM algorithm,that is,θ(t)satisfiesF(θ(t)|˜θ(t−1))=0,(10) where,in similar fashion to above,we define F(θ|θ )=∂Q(θ|θ )/∂θ.Thus,afirst order Taylor expansion of˜F P(˜θ(t)|˜θ(t−1))aboutθ(t)yields(˜θ(t)−θ(t))T˜S P(θ(t)|˜θ(t−1))≈−˜F P(θ(t)|˜θ(t−1)),(11)where we define the matrix˜S P(θ|θ )=∂2˜Q P(θ|θ )/∂θ∂θT.Under RQMC sampling,˜Q P is an unbiased estimate of Q.Assuming mild regularity conditions,it follows that for the expectationE[˜F P(θ(t)|˜θ(t−1))]=F(θ(t)|˜θ(t−1))=0.(12) Therefore,the expected value of˜θ(t)isθ(t)and its variance-covariance matrix is given byVar(˜θ(t))=˜S P(θ(t)|˜θ(t−1))−1Var(˜F P(θ(t)|˜θ(t−1))˜S P(θ(t)|˜θ(t−1))−1.(13)Under regular Monte Carlo sampling,it follows that,for a large enough Monte Carlo sample size,˜θ(t)is approximately normal distributed with mean and variance specified above.Under RQMC sampling,however,the accuracy of the normal approximation may depend on the number r of independent RQMC sequences.In Section4we consider a range of values for r in order to investigate its effect on QMCEM.In our implementations we estimate Var(˜θ(t))by substituting˜θ(t)forθ(t)in(13)and estimate Var(˜F P(θ(t)|˜θ(t−1))via1 r2rj=1∂∂θ˜Q(j)(θ|˜θ(t−1))∂∂θ˜Q(j)(θ|˜θ(t−1))T=˜ (t).(14)Larger values of r should result in a more accurate estimate for Var(˜θ(t)).However,we also pointed out that smaller values of r should result in a better accuracy of the Monte Carlo estimate in(8),since it takes better advantage of the low-discrepancy property of each individual sequence u(j)1,...,u(j)mt.We investigate the impact of this trade-offon the overall efficiency of the method in Section4.The QMCEM algorithm proceeds as follows.Following Booth&Hobert’s recommendation,we measure the noise in the QMCEM update˜θ(t)by constructing a(1−α)×100%confidence ellipsoid about the deterministic EM updateθ(t),using the normal approximation for˜θ(t).If this ellipsoid contains the previous parameter value˜θ(t−1),then we conclude that the system is too noisy and we increase the length m t of the RQMC sequence.Booth et al.(2001)argue that the sample sizes should be increased at an exponential rate.Thus,we increase the sample size to m t+1:=(1+κ)m t, whereκis a small number,typicallyκ=0.2,0.3,0.4.Since stochastic algorithms,like MCEM,can satisfy deterministic stopping rules purely by chance,it is recommended to continue the method until the stopping rule is satisfied for several consecutive iterations(see also Booth&Hobert,1999).Thus,we stop the algorithm when the relative change in two successive parameter updates is smaller than some small numberδ,δ>0,for3consecutive iterations.3.2Laplace Importance Sampling to generate RQMC sequences,j= Recall that the pooled estimate in(8)is based on r independent RQMC sequences u(j)1,...,u(j)mt 1,...,r,simulated from f(u|y;θ(t−1)).In this section we demonstrate how to generate randomized Halton sequences using Laplace importance sampling.Laplace importance sampling has been proven useful to draw approximate samples from f(u|y;θ) in many instances(see Booth&Hobert,1999;Kuk,1999).Laplace importance sampling attempts tofind an importance sampling distribution whose mean and variance match the mode and curva-ture of f(u|y;θ).More specifically,suppressing the dependence on y,letl(u;θ)=log f(y,u;θ)(15)denote the complete data log likelihood and let l (u;θ)and l (u;θ)denote itsfirst and second derivatives in u,respectively.Suppose that˜u denotes the maximizer of l satisfying l (u;θ)= 0.Then the Laplace approximations to the mean and variance of f(u|y;θ)areµ(θ)=˜u and Σ(θ)=−{l (˜u;θ)}−1,respectively(e.g.De Bruijn,1958).Booth&Hobert(1999)as well as Kuk (1999)propose to use a multivariate normal or multivariate t importance sampling distribution, shifted and scaled byµ(θ)andΣ(θ),respectively.Let f Lap(u|y;θ)denote the resulting Laplace importance sampling distribution.Recall that by RQMC property1),every element of a RQMC sequence has a uniform distri-bution over C d.Let x k be the k th element of a randomized Halton ing a suitable transformation(e.g.Robert&Casella,1999),we can generate a d-vector of i.i.d.normal or t vari-ates.Shifting and scaling this vector byµ(θ)andΣ(θ)results in a draw u k from f Lap(u|y;θ).,j=1,...,r, Thus,using r independent randomized Halton sequences of length m t,x(j)1,...,x(j)mtfrom f Lap(u|y;θ).we obtain r independent sequences u(j)1,...,u(j)mtBooth&Hobert(1999)or Kuk(1999)successfully use Laplace importance sampling for the fitting of generalized linear mixed models.In the following we use the method to an application of generalized linear mixed models to data exhibiting spatial correlation.4Application:A Geostatistical Model of Online PurchasesIn this section we consider sales data from an online book publisher and retailer.The publisher sells online the titles it publishes in print form as well as,more recently,also in PDF form.The publisher has good reason to believe that a customer’s preference for either print or PDF form varies significantly due to his or her geographical location.In fact,since the PDF form is directly downloaded from the publisher’s web site,it requires a reliable and typically fast internet connec-tion.However,the availability of reliable internet connections varies greatly across different regions. Moreover,directly downloaded PDFfiles provide content immediately without having to wait for shipment as in the case of a printed book.Thus,shipping times can also influence a customer’s preference.The preference can also be affected by a customer’s access to good quality printers or his/her technology readiness,all of which often exhibit strong local variability.Data exhibiting spatial correlation can be modelled using generalized linear mixed models(e.g Breslow&Clayton,1993).Diggle et al.(1998)refer to these spatial applications of generalized linear mixed models as“model based geostatistics.”These spatial mixed models are challenging from a computational point of view since they often involve approximating rather high dimensional integrals.In the following we consider a set of data leading to an analytically intractable likelihood-integral of dimension16.Let{z i}d i=1,z i=(z i1,z i2),denote the spatial coordinates of the observed responses{y i}d i=1. For example,z i1and z i2could denote the longitude and latitude of the observation y i.While y i could represent a variety of response types,we focus here on the binomial case only.For instance, y i could indicate whether or not a person living at location z i has a certain disease or whether or not this person has a preference for a certain product.One of the modelling goals is to account for the possibility that two people living in close geographic proximity are more likely to share the same disease or the same preference.Let u=(u1,...,u d)be a vector of random effects.Assume that,conditional on u i,the responsesy i arise from the modely i|u i∼Binomialn i,exp(β+u i)1+exp(β+u i),(16)whereβis an unknown regression coefficient.Assume furthermore that u follows a multivariate normal distribution with mean zero and covariance structure such that the correlation between tworandom effects decays with the geographical distance between the associated two observations.For example,assume thatCov(u i,u j)=σ2exp{−α z i−z j },(17) where · denotes the Euclidian norm.While different modelling alternatives exist(see,for example, Diggle et al.,1998),we will use the above model to investigate the efficiency of Quasi-Monte Carlo MCEM implementations for estimating the parameter vectorθ=(β,σ2,α).We analyze a set of online retail data for the Washington,DC,area.Washington is a very diverse area with respect to a variety of aspects like socio-economic factors or infrastructure.This diversity is often expressed in regionally/locally strongly varying customer preferences.The data set consists of39customers who accessed the publisher’s web site and either purchased the title in print form or in PDF.In addition to a customer’s purchasing choice,the publisher also recorded the customer’s geographical location.Geographical location can easily be obtained(at least ap-proximately)through the customer’s ZIP code.ZIP code information can then be transformed into longitudinal and latitudinal coordinates.After aggregating customers from the same ZIP code with the same preference,we obtained d=16distinct geographical locations.Let n i denote the number of purchases from location i and let y i denote the number of PDF purchases thereof.Figure2 displays the data.Figure2about hereQuasi-Monte Carlo has been found to improve upon the efficiency of classical Monte Carlo methods in a variety of setting.For instance,Bhat(2001)reports efficiency gains via the Halton sequence in a logit model for integral dimensions ranging from1to5.Lemieux&L’Ecuyer(1998), on the other hand,consider integral dimensions as large as120andfind efficiency improvements for the pricing of Asian options.In our example,the correlation structure of the random effects in equation(17)causes the likelihood function(and therefore also the E-step of the EM algorithm) to include an analytically intractable integral of dimension16.Indeed,the(marginal)likelihood function for the model in(16)and(17)can be written asL(θ;y)∝di=1f(y i|u i;θ)exp{−0.5u TΣ−1u}|Σ|d u,(18)where u=(u1,...,u16)T contains the random effects corresponding to the16distinct locations andΣis a16×16matrix with elementsσij=Cov(u i,u j).The evaluation of high dimensional integrals is computationally burdensome.We conducted a simulation study to investigate the efficiency of QMC approaches relative to that of classical Monte Carlo.Table1shows the results for three different QMCEM algorithms,using r=5, r=10and r=30RQMC sequences,respectively.This compares to an implementation of MCEM using classical Monte Carlo techniques.We can see that the Monte Carlo standard errors of the parameter estimates ofθ=(β,σ2,α)are very similar across the estimation methods,indicating that all4methods estimate the parameters with(on average)comparable accuracy.However, the total simulation efforts required to obtain this accuracy differs greatly.Indeed,while classical Monte Carlo requires an average number of800,200simulated vectors(each of dimension16!), it only takes20,836for QMC(using r=5RQMC sequences).This is a reduction in the total simulation effort by a factor of almost40!It is also interesting to note that among the3different QMC approaches,choosing r=30RQMC sequences results in a(average)total simulation effort of30,997simulated vectors compared to only20,836for r=5.Table1about hereThe reduction in the total simulation effort that is possible with the use of QMC methods is intriguing.The MCEM algorithm usually spends most of its simulation effort in thefinal iterations when the algorithm is in the vicinity of the MLE.This has already been observed by,for example, Booth&Hobert(1999)or McCulloch(1997).The reason for this is the convergence behavior of the underlying deterministic EM algorithm.EM usually takes large steps in the early iterations, but the size of the steps reduce drastically as EM approachesˆθ.The step size in the t th iteration of EM can be thought of as the signal that is transmitted to MCEM.However,due to the error in the Monte Carlo approximation of the E-step in(7),MCEM receives only a noisy version of that signal.While the signal-to-noise ratio is large in the early iterations of MCEM,it declines continuously as MCEM approachesˆθ.This makes larger Monte Carlo sample sizes necessary in order to increase the accuracy of the approximation in(7)and therefore to reduce the noise.Table 1shows that QMC methods,due to their superior ability to estimate an intractable integral more accurately,manage to reduce that noise with smaller sample sizes.The result is a smaller total simulation effort required by QMC.Table1also shows that among the3different QMCEM algorithms,implementations that use fewer but longer low-discrepancy sequences result in a better total simulation effort than a largenumber of short sequences.Indeed,the simulation effort for r=30RQMC sequences is about50% higher than that for r=5or r=10.We pointed out in Section2that for a given total simulation amount r·m,smaller values of r paired with larger values of m should result in a more accurate integral-estimate.On the other hand,the trade-offfor using small values of r is a less accurate variance estimate in(14).In order to implement MCEM using randomized Halton sequences,a balance has to be achieved between a more accurate integral-estimate(i.e.less noise)and a more accurate variance estimate.In our example,we found this balance for values of r between5and 10.We also experimented with values smaller than5and frequently encountered problems with the numerical stability of the estimate of the covariance matrix in(14).In thefinal paragraphs of this section we want to take a closer look at noise of the QMCEM algorithm and compare it to classical MCEM.Figure3visualizes the Monte Carlo error for three different Monte Carlo estimation methods:classical Monte Carlo using random sampling(column 1),randomized Quasi-Monte Carlo with r=5RQMC sequences(column2)and pure Quasi-Monte Carlo without randomization(column3).Figure3about hereWe can see that for classical Monte Carlo,the average parameter update(thick solid line)is very volatile and has wide confidence bounds(dotted lines).This suggests that the Monte Carlo error is huge.This is in strong contrast to QMC.Indeed,for pure QMC sampling the parameter updates are significantly less volatile with much tighter confidence bounds.Notice that we allocated the same simulation effort for both simulation methods!It takes classical MCEM much larger sample sizes to reduce the noise to the same level as under QMC sampling.We have argued at the beginning of this paper that in order to implement MCEM in an au-tomated way,the ability to estimate the error in the Monte Carlo approximation is essential. Randomized QMC methods provide this ability.While randomized Halton sequences have the low-discrepancy property(and thus estimate the integral with a higher accuracy than classical Monte Carlo),randomization may not come for free.Indeed,the second column of Figure3shows that, while the error reduction is still substantial compared to a classical Monte Carlo approach,the system is noisier than under pure QMC sampling.。
Monte Carlo方法计算辐射
第1章绪论1.1课题研究背景和意义1.1.1 课题研究的背景随着国民经济的高速发展,人民生活水平不断提高,为了创造一个舒适的生活环境,人们普遍采用了各种各样的空调器、采暖设备等等。
在冬天,各种各样的采暖设备被广泛使用,如暖气片、辐射板、采暖空调、电加热器等等。
能源消耗日益巨大,在某些地方由于使用烧煤、烧气等采暖,造成了一定的环境污染。
因此,需要一种即节能,且无污染的采暖设备。
辐射板是一种节能,而且没有污染的采暖设备,在欧洲等发达国家,已经被大量使用。
在中国,北京某些新建的住宅楼也采用了辐射板采暖。
有的公司还开发出具有保健作用的辐射板,市场潜力巨大。
以前计算辐射换热大多采用分析解法,如微分法、积分法、代数法等等。
对于一些比较简单的几何表面,上述方法可以解决。
但是在实际情况中,由于各表面之间的复杂性,分析解法很难解决这些问题。
目前,随着计算科学的不断发展,在工程上,开始将数值计算法应用于辐射角系数的计算。
1.1.2 课题研究的目的和意义本论文研究的目的就是计算出辐射板位置对人体舒适度的影响。
1984年国际标准化组织(ISO),在ISO7730标准[1]中,用PMV和PPD指标来描述和评价热环境。
在指标综合考虑了人体活动情况、着衣情况、空气温度、湿度、流速、平均辐射温度等六个因素。
在稳态热环境下,丹麦工业大学的Fanger[1,2]提出PMV-PPD评价方法,该法是以下列热平衡式为基础的:人体产热-对外做功消耗-体表扩散失热-汗液蒸发失热-呼吸的显热和潜热交换=通过衣服的换热=在热换境内通过对流和辐射的换热本论文用Monte Carlo方法计算出在热环境内的辐射换热,这是最主要的工作。
然后计算对流换热和其他人体散热,从而计算出人体舒适度。
不同的辐射板布置方式就有不同的辐射换热,人体舒适度也不同。
确定一个较佳的辐射板布置位置,从而使PMV值在-0.5至0.5之间,使人感到舒适。
1.2 国内外研究综述由于分析解法的复杂性,很多工程上的辐射换热问题都难以解决。
周期性Freedericksz形变的Monte Carlo模拟
河北工业大学 硕士学位论文 周期性Freedericksz形变的Monte Carlo模拟 姓名:韩超 申请学位级别:硕士 专业:理论物理 指导教师:张志东
20091201
河北工业大学硕士学位论文
周期性 Freedericksz 形变的 Monte Carlo 模拟
摘要
当向列相液晶位于两片经过表面处理的玻璃片之间时,使沿玻璃表面的液晶指向矢 具有平行于盒表面的确定取向。在垂直于表面的方向(z 方向)加一外电场,当外场强度增 加且超过某一临界值,指向矢将偏离 xy 平面。一般的,此偏离夹角与 x,y 无关,这样的 形变被称为均匀形变。然而,理论计算和实验表明当扭曲弹性系数与展曲弹性系数的比 值满足一定关系时,系统不产生均匀形变,而进入周期性形变。在此形变中,指向矢沿 平行于表面的某一方向周期性变化。
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PACS-numbers: 64.60.Fr, 64.70.Rh, 75.10.Hk, 75.40.Mg Keywords: Ising models, antiferromagnets, correlation functions, Monte
Carlo simulations, nite-size scaling.
0 2 3 0
2
emerge. Using topological theorems Kasteleyn 6, 7] evaluated the con gurational generating function of the problem covering a lattice with dimers. The success of this approach relies on the fact that the combinatorics can be lumped into a mathematical entity known as the Pfa an, which is simply the square root of a determinant. The Ising problem is then addressed by counting dimer con gurations on a modi ed lattice, in which every vertex has been substituted by a suitably oriented polygon in order to ensure a correct counting of the states summed over in the Ising partition function 7, 8]. This scheme (as opposed to the algebraic method) is easily augmented to include the evaluation of correlation functions 8] expressed as Toeplitz determinants. Using asymptotic properties for these objects, Stephenson 9] provided the long-distance behaviour of the twospin correlations along the three main directions for the TAI model with 10] or without 11] anisotropy. A recent interest 12, 13, 14, 15, 16, 17, 18] has been taken in generalising the Hamiltonian (1) to include ferromagnetic next-nearest neighbour interactions as well as anisotropy. Such additional couplings tend to lift the ground state degeneracy, since, dividing the triangular lattice into the usual three sublattices, next-nearest neighbour pairs belong to the same of these sublattices (see Fig. 1). By applying mappings which are constructed to automatically satisfy the antiferromagnetic nearest neighbour constraint, the ground state ensemble can be investigated within the context of solid-on-solid (SOS) 12, 13, 15] or domain-wall 16, 17] models. In this paper we shall concern ourselves with the nature of the correlations in the lowtemperature TAI model. Despite the fact that the abundance of entropy prevents the emergence of a long-range order, even at zero temperature 2], the existence of some kind of short-range order or pattern formation should be evident from a visual inspection of Fig. 2, and the T = 0 state is indeed a critical one as witnessed by the algebraic decay of correlations 11]. We shall study this ordering by means of the structure factor 3
Typeset using REVTEX 1
Since Onsager's evaluation of the free energy for the Ising-model on a rectangular lattice 1] the corresponding model on a triangular lattice has served as an afterthought requiring some modi cation of the analysis 2]. The Triangular Antiferromagnetic Ising (TAI) model has also attracted attention in its own right, it being a simple manifestation of a fully frustrated system 3]. Very recently, the interest in the TAI model has been revived through its near-perfect experimental realisation in the yavapaiite layered structure of anhydrous alums such as RbFe(SO ) 4]. Mathematically the isotropic TAI model to be studied in this paper is de ned by i;j i
sisj ;
(1)
where J > 0 is the antiferromagnetic exchange coupling and si = 1 are Ising spins on the triangular lattice depicted in Fig. 1. The label hi; j i indicates a sum over nearest neighbour pairs each pair being counted only once. Frustration in the ground state arises from the inability of the system to simultaneously satisfy the \local packing rule" that three spins on an elementary triangle are pairwise each other's nearest neighbour, and the \global packing constraint" that, in order to minimise energy, the ground state must have as many antiferromagnetically satis ed nearest neighbour bonds as possible. In other words, it is impossible to orient three spins in a pairwise antiparallel fashion. It can thus be shown 2] that the ground state is macroscopically degenerate with a nite entropy per spin s = kB R = ln(2 cos !) d! ' 0:323 kB : In Wannier's original approach 2] the method of Onsager (diagonalisation of the transfer matrix using representation theory on an associated Lie algebra) was modi ed to include the diagonal interactions of the TAI model. Due to the technical complexity of this method much work was done to achieve a simpli cation of the algebra involved (see references in 5]), the result being the now well-known technique of reducing the problem to chains of interacting fermions 5]. At the same time an alternative scheme, known as the combinatorial method, began to