蒙特卡罗统计方法

《蒙特卡罗统计方法》是2009阶功做季观年10月由世界图书出版公司出版的图书。本书主要介绍了蒙特卡罗统计方法的概念和各类理论方法等。

• 书名 蒙特卡罗统计方法
• 页数 645页
• 出版社 世界图书出版公司;
• 出版时间 2009年10月1日

图书信息

　　外文书名: Monte Carlo Statistical Me热饭父队thods (2nd Edi职在众伯降死tion)

　　平装

消诉　　正文语种: 英语

　　开本: 24

　　ISBN: 9787510005114, 7510005116

　　条形码: 航低达办福环存978751000来自5114

　　尺寸: 22 x 15 x 3 cm

　　重量: 980 g

作者简介

　　作者:(法国)罗伯特(C感并聚推此光卷包hristian P.Robert) (法国)George Casella

内容简介

　　《蒙特卡罗统计方法(第2版)(英文版)》内容简介:Introduction、Statistical Models、Likelihood Method会略吃烈s、Bayesian Me目殖春拿thods、Determini花握明数无左尔官stic Numerical Methods、Optimization、来自Integration360百科、Comparison、Problems、Notes、Prior Distributions、Bootstrap Methods、Random Variable Generation、In刻教troduction、Uniform Simulation、The Inverse Transform、Alternatives、Optimal Algorithms、General Transformation Methods、Accept-Reject Methods、The Fundamental Theorem of Simulation、The Accept-Reject Algorithm、Envelope Accept-Reject Methods、The Sq投在就众管身继机民ueeze Princ到鸡iple、Log-才表德止复Concave Densit烈否山以乡风句调吃ies等等。

目录

　　Preface to the Second Edition

　　Preface to the First 蒸必造好理Edition

　　1 Introduction

　　1.1 Statistical Models

　　1.2 Likelihood Methods

　　1.3 Bayesian Me修内英冲天继愿想老正厚thods

　　1.4 Deterministic Numerical Methods

　　1.4.1 Optimization

　　1.4.2 Integration

　　1.4.3 Co滑终脱儿象似自置判场mparison

　　参械动送续度积划企临1.5 Problems

　　色象生低屋1.6 Notes

　　1.6座获充庆略食速刑岁.1 Prior Dist呀校ributions

　　1促光绝乎就声自迅执草单.6.2 Bootstrap Me请轴块介东点thods

　　2 Random Variable Generati书笔汉州女千断际景乎on

　　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