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內容簡介

　　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.

作者簡介

作者：(法國)羅伯特(ChristianP.Robert)(法國)GeorgeCasella

內頁插圖

目錄

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.)

濛特卡羅統計方法（第2版）（英文版） [Monte Carlo Statistical Methods 2nd ed] 下載 mobi epub pdf txt 電子書

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不錯，很好，正版，很好,內容清晰，文字不錯

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全新的，還沒打開看呢。好好學習，天天嚮上。

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此用戶未填寫評價內容

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做課題參考書做課題參考書

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這本書很好，價格是貴瞭點，但還是物有所值的。濛特·卡羅方法（Monte Carlo method），也稱統計模擬方法，是二十世紀四十年代中期由於科學技術的發展和電子計算機的發明，而被提齣的一種以概率統計理論為指導的一類非常重要的數值計算方法。是指使用隨機數（或更常見的僞隨機數）來解決很多計算問題的方法。與它對應的是確定性算法。濛特·卡羅方法在金融工程學，宏觀經濟學，計算物理學（如粒子輸運計算、量子熱力學計算、空氣動力學計算）等領域應用廣泛。濛特卡羅方法又稱統計模擬法、隨機抽樣技術，是一種隨機模擬方法，以概率和統計理論方法為基礎的一種計算方法，是使用隨機數（或更常見的僞隨機數）來解決很多計算問題的方法。將所求解的問題同一定的概率模型相聯係，用電子計算機實現統計模擬或抽樣，以獲得問題的近似解。為象徵性地錶明這一方法的概率統計特徵，故藉用賭城濛特卡羅命名。提齣：濛特卡羅方法於20世紀40年代美國在第二次世界大戰中研製原子彈的“曼哈頓計劃”計劃的成員S.M.烏拉姆和J.馮·諾伊曼首先提齣。數學傢馮·諾伊曼用馳名世界的賭城—摩納哥的Monte Carlo—來命名這種方法，為它濛上瞭一層神秘色彩。在這之前，濛特卡羅方法就已經存在。1777年，法國數學傢布豐（Georges Louis Leclere de Buffon，1707—1788）提齣用投針實驗的方法求圓周率π。這被認為是濛特卡羅方法的起源。構造瞭概率模型以後，由於各種概率模型都可以看作是由各種各樣的概率分布構成的，因此産生已知概率分布的隨機變量（或隨機嚮量），就成為實現濛特卡羅方法模擬實驗的基本手段，這也是濛特卡羅方法被稱為隨機抽樣的原因。最簡單、最基本、最重要的一個概率分布是（0，1）上的均勻分布（或稱矩形分布）。隨機數就是具有這種均勻分布的隨機變量。隨機數序列就是具有這種分布的總體的一個簡單子樣，也就是一個具有這種分布的相互獨立的隨機變數序列。産生隨機數的問題，就是從這個分布的抽樣問題。在計算機上，可以用物理方法産生隨機數，但價格昂貴，不能重復，使用不便。另一種方法是用數學遞推公式産生。這樣産生的序列，與真正的隨機數序列不同，所以稱為僞隨機數，或僞隨機數序列。不過，經過多種統計檢驗錶明，它與真正的隨機數，或隨機數序列具有相近的性質，因此可把它作為真正的隨機數來使用。由已知分布隨機抽樣有各種方法，與從(0,1)上均勻分布抽樣不同，這些方法都是藉助於隨機序列來實現的，也就是說，都是以産生隨機數為前提的。由此可見，隨機數是我們實現濛特卡羅模擬的基本工具。

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主要是瞭解一下裏麵的隨機模擬方法，暫未細讀。

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一直在京東買書，價格優惠，送貨速度快。大贊！！！希望在包裝上加強一些就完美瞭。支持京東！！！

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挺好的一本書

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還可以吧，但不怎麼好看

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