蒙特卡罗统计方法（第2版）（英文版） [Monte Carlo Statistical Methods 2nd ed] epub pdf mobi txt 电子书 下载 2025

蒙特卡罗统计方法（第2版）（英文版） [Monte Carlo Statistical Methods 2nd ed] epub pdf mobi txt 电子书 下载 2025

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蒙特卡罗统计方法（第2版）（英文版） [Monte Carlo Statistical Methods 2nd ed] epub pdf mobi txt 电子书 下载 2025

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内容简介

　　It is a tribute to our profession that a textbook that was current in 1999 is starting to feel old. The work for the first edition of Monte Carlo Statistical Methods (MCSM1) was finished in late 1998, and the advances made since then, as well as our level of understanding of Monte Carlo methods, have grown a great deal. Moreover, two other things have happened. Topics that just made it into MCSM1 with the briefest treatment (for example, perfect sampling) have now attained a level of importance that necessitates a much more thorough treatment. Secondly, some other methods have not withstood the test of time or, perhaps, have not yet been fully developed, and now receive a more appropriate treatment.
　　When we worked on MCSM1 in the mid-to-late 90s, MCMC algorithms were already heavily used, and the flow of publications on this topic was atsuch a high level that the picture was not only rapidly changing, but also necessarily incomplete. Thus, the process that we followed in MCSM1 was that of someone who was thrown into the ocean and was trying to grab onto the biggest and most seemingly useful objects while trying to separate the flotsam from the jetsam. Nonetheless, we also felt that the fundamentals of many of these algorithms were clear enough to be covered at the textbook alevel, so we" swam on.

作者简介

作者：(法国)罗伯特(ChristianP.Robert)(法国)GeorgeCasella

内页插图

目录

Preface to the Second Edition
Preface to the First Edition
1 Introduction
1.1 Statistical Models
1.2 Likelihood Methods
1.3 Bayesian Methods
1.4 Deterministic Numerical Methods
1.4.1 Optimization
1.4.2 Integration
1.4.3 Comparison
1.5 Problems
1.6 Notes
1.6.1 Prior Distributions
1.6.2 Bootstrap Methods

2 Random Variable Generation
2.1 Introduction
2.1.1 Uniform Simulation
2.1.2 The Inverse Transform
2.1.3 Alternatives
2.1.4 Optimal Algorithms
2.2 General Transformation Methods
2.3 Accept-Reject Methods
2.3.1 The Fundamental Theorem of Simulation
2.3.2 The Accept-Reject Algorithm
2.4 Envelope Accept-Reject Methods
2.4.1 The Squeeze Principle
2.4.2 Log-Concave Densities
2.5 Problems
2.6 Notes
2.6.1 The Kiss Generator
2.6.2 Quasi-Monte Carlo Methods
2.6.3 Mixture RepresentatiOnS

3 Monte Carlo Integration
3.1 IntroduCtion
3.2 Classical Monte Carlo Integration
3.3 Importance Sampling
3.3.1 Principles
3.3.2 Finite Variance Estimators
3.3.3 Comparing Importance Sampling with Accept-Reject
3.4 Laplace Approximations
3.5 Problems
3.6 Notes
3.6.1 Large Deviations Techniques
3.6.2 The Saddlepoint Approximation

4 Controling Monte Carlo Variance
4.1 Monitoring Variation with the CLT
4.1.1 Univariate Monitoring
4.1.2 Multivariate Monitoring
4.2 Rao-Blackwellization
4.3 Riemann Approximations
4.4 Acceleration Methods
4.4.1 Antithetic Variables
4.4.2 Contr01 Variates
4.5 Problems
4.6 Notes
4.6.1 Monitoring Importance Sampling Convergence
4.6.2 Accept-Reject with Loose Bounds
4.6.3 Partitioning

5 Monte Carlo Optimization
5.1 Introduction
5.2 Stochastic Exploration
5.2.1 A Basic Solution
5.2.2 Gradient Methods
5.2.3 Simulated Annealing
5.2.4 Prior Feedback
5.3 Stochastic Approximation
5.3.1 Missing Data Models and Demarginalization
5.3.2 Thc EM Algorithm
5.3.3 Monte Carlo EM
5.3.4 EM Standard Errors
5.4 Problems
5.5 Notes
5.5.1 Variations on EM
5.5.2 Neural Networks
5.5.3 The Robbins-Monro procedure
5.5.4 Monte Carlo Approximation

6 Markov Chains
6.1 Essentials for MCMC
6.2 Basic Notions
6.3 Irreducibility，Atoms，and Small Sets
6.3.1 Irreducibility
6.3.2 Atoms and Small Sets
6.3.3 Cycles and Aperiodicity
6.4 Transience and Recurrence
6.4.1 Classification of Irreducible Chains
6.4.2 Criteria for Recurrence
6.4.3 Harris Recurrence
6.5 Invariant Measures
6.5.1 Stationary Chains
6.5.2 Kac’s Theorem
6.5.3 Reversibility and the Detailed Balance Condition
6.6 Ergodicity and Convergence
6.611 Ergodicity
6.6.2 Geometric Convergence
6.6.3 Uniform Ergodicity
6.7 Limit Theorems
6.7.1 Ergodic Theorems
6.7.2 Central Limit Theorems
6.8 Problems
6.9 Notes
6.9.1 Dri允Conditions
6.9.2 Eaton’S Admissibility Condition
6.9.3 Alternative Convergence Conditions
6.9.4 Mixing Conditions and Central Limit Theorems
6.9.5 Covariance in Markov Chains

7 The Metropolis-Hastings Algorithm
7.1 The MCMC Principle
7.2 Monte Carlo Methods Based on Markov Chains
7.3 The Metropolis-Hastings algorithm
7.3.1 Definition
7.3.2 Convergence Properties
7.4 The Independent Metropolis-Hastings Algorithm
7.4.1 Fixed Proposals
7.4.2 A Metropolis-Hastings Version of ARS
7.5 Random walks
7.6 Optimization and Contr01
7.6.1 Optimizing the Acceptance Rate
7.6.2 Conditioning and Accelerations
7.6.3 Adaptive Schemes
7.7 Problems
7.8 Nores
7.8.1 Background of the Metropolis Algorithm
7.8.2 Geometric Convergence of Metropolis-Hastings Algorithms
7.8.3 A Reinterpretation of Simulated Annealing
7.8.4 RCference Acceptance Rates
7.8.5 Langevin Algorithms

8 The Slice Sampler
8.1 Another Look at the Fundamental Theorem
8.2 The General Slice Sampler
8.3 Convergence Properties of the Slice Sampler
8.4 Problems
8.5 Notes
8.5.1 Dealing with Di伍cult Slices

9 The Two－Stage Gibbs Sampler
9.1 A General Class of Two-Stage Algorithms
9.1.1 From Slice Sampling to Gibbs Sampling
9.1.2 Definition
9.1.3 Back to the Slice Sampler
9.1.4 The Hammersley-Clifford Theorem
9.2 Fundamental Properties
9.2.1 Probabilistic Structures
9.2.2 Reversible and Interleaving Chains
9.2.3 The Duality Principle
9.3 Monotone Covariance and Rao-Btackwellization
9.4 The EM-Gibbs Connection
9.5 Transition
9.6 Problems
9.7 Notes
9.7.1 Inference for Mixtures
9.7.2 ARCH Models

10 The Multi-Stage Gibbs Sampler
10.1 Basic Derivations
10.1.1 Definition
10.1.2 Completion
……
11 Variable Dimension Models and Reversible Jump Algorithms
12 Diagnosing Convergence
13 Perfect Sampling
14 Iterated and Sequential Importance Sampling
A Probability Distributions
B Notation
References
Index of Names
Index of Subjects

前言/序言

　　He sat，continuing to look down the nave，when suddenly the solution to the problem just seemed to present itself．It was so simple，SO obvious he just started to laugh——P．C．Doherty．Satan in St Marys
　　Monte Carlo statistical methods，particularly those based on Markov chains，have now matured to be part of the standard set of techniques used by statisticians．This book is intended to bring these techniques into the classroom． being(we hope)a self-contained logical development of the subject，with all concepts being explained in detail．and all theorems．etc．having detailed proofs．There is also an abundance of examples and problems，relating the concepts with statistical practice and enhancing primarily the application of simulation techniques to statistical problems of various difficulties．
　　This iS a textbook intended for a second-year graduate course．We do not assume that the reader has any familiarity with Monte Carlo techniques (such as random variable generation)or with any Markov chain theory. We do assume that the reader has had a first course in statistical theory at the level of Statistica!Inference bY Casella and Berger(1990)．Unfortunately，a few times throughout the book a somewhat more advanced notion iS needed．We have kept these incidents to a minimum and have posted warnings when they occur．While this iS a book on simulation．whose actual implementation must be processed through a computer，no requirement lS made on programming skills or computing abilities：algorithms are presented in a program-like format but in plain text rather than in a specific programming language．(Most of the examples in the book were actually implemented in C．with the S-Plus graphical interface.)

蒙特卡罗统计方法（第2版）（英文版） [Monte Carlo Statistical Methods 2nd ed] epub pdf mobi txt 电子书 下载 2025

蒙特卡罗统计方法（第2版）（英文版） [Monte Carlo Statistical Methods 2nd ed] 下载 epub mobi pdf txt 电子书 2025

评分☆☆☆☆☆

书是文化、精神食粮，好东西

评分☆☆☆☆☆

这个方法在统计学，数值模拟上都很有用！专著！！

评分☆☆☆☆☆

刚刚入手,准备学习

评分☆☆☆☆☆

统计学的教材，多年的经典

评分☆☆☆☆☆

还没看，备用先，书总是越多越好

评分☆☆☆☆☆

买了备着，英文的书看起来还是有点吃力，要是有翻译版就行了

评分☆☆☆☆☆

这本书很好，价格是贵了点，但还是物有所值的。蒙特·卡罗方法（Monte Carlo method），也称统计模拟方法，是二十世纪四十年代中期由于科学技术的发展和电子计算机的发明，而被提出的一种以概率统计理论为指导的一类非常重要的数值计算方法。是指使用随机数（或更常见的伪随机数）来解决很多计算问题的方法。与它对应的是确定性算法。蒙特·卡罗方法在金融工程学，宏观经济学，计算物理学（如粒子输运计算、量子热力学计算、空气动力学计算）等领域应用广泛。蒙特卡罗方法又称统计模拟法、随机抽样技术，是一种随机模拟方法，以概率和统计理论方法为基础的一种计算方法，是使用随机数（或更常见的伪随机数）来解决很多计算问题的方法。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。提出：蒙特卡罗方法于20世纪40年代美国在第二次世界大战中研制原子弹的“曼哈顿计划”计划的成员S.M.乌拉姆和J.冯·诺伊曼首先提出。数学家冯·诺伊曼用驰名世界的赌城—摩纳哥的Monte Carlo—来命名这种方法，为它蒙上了一层神秘色彩。在这之前，蒙特卡罗方法就已经存在。1777年，法国数学家布丰（Georges Louis Leclere de Buffon，1707—1788）提出用投针实验的方法求圆周率π。这被认为是蒙特卡罗方法的起源。构造了概率模型以后，由于各种概率模型都可以看作是由各种各样的概率分布构成的，因此产生已知概率分布的随机变量（或随机向量），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。最简单、最基本、最重要的一个概率分布是（0，1）上的均匀分布（或称矩形分布）。随机数就是具有这种均匀分布的随机变量。随机数序列就是具有这种分布的总体的一个简单子样，也就是一个具有这种分布的相互独立的随机变数序列。产生随机数的问题，就是从这个分布的抽样问题。在计算机上，可以用物理方法产生随机数，但价格昂贵，不能重复，使用不便。另一种方法是用数学递推公式产生。这样产生的序列，与真正的随机数序列不同，所以称为伪随机数，或伪随机数序列。不过，经过多种统计检验表明，它与真正的随机数，或随机数序列具有相近的性质，因此可把它作为真正的随机数来使用。由已知分布随机抽样有各种方法，与从(0,1)上均匀分布抽样不同，这些方法都是借助于随机序列来实现的，也就是说，都是以产生随机数为前提的。由此可见，随机数是我们实现蒙特卡罗模拟的基本工具。

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帮人买的 给个好评吧， 发货很快

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　　上边是之前的评论，那时刚刚开始看此书，现在半年过去了(2012.8.30)，谈谈自己新的看法。本书存在几个问题：

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