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大致看了看，非常好的一份材料，不仅研究了Monte Carlo,还研究了Bayesian，非常值得大家看哦，不过缺点就在于这份PDF没有目录，所以我为这个材料做了一份目录，希望可以帮助大家学习：

Monte Carlo Statistical Methods

by Christian P.Robert

context

1 introduction

1.1 Statistical Models 5

1.2 Likelihood Methods 10

1.3 Bayesian Methods 21

1.4 Deterministic Numerical Methods 28

1.5 simulation versus numerical analysis:

when is it useful? 31

2 Random Variable Generation 35

2.1 Basic Methods 37

2.2 Beyond uniform distributions 51

3 Monte Carlo Intergration 76

3.1 introduction 77

3.2 Classical Monte Carlo Integration 80

3.3 importance sampling 87

3.4 Acceleration Methods 98

4 Markov Chains 108

4.1 Basic Notions 110

4.2 Irreducibility 115

4.3 Transience/Recurrence 123

4.4 Invariant Measures 126

4.5 Ergodicity and stationarity 130

4.6 Limit Theorems 134

5 Monte Carlo Optimization 139

5.1 Introduction 140

5.2 Stochastic Exploration 142

5.3 Stochastic Approximation 173

5.3.3 MCEM 195

6 The Metropolis-Hastings Algorithm

6.1 Markov Chain Monte Carlo 197

6.2 The Metropolis-Hastings Algorithm 199

6.3 A Collection of Metropolis-Hastings Algorithms 204

6.4 Extensions 217

7 The Gibbs Sampler 231

7.1 General Principles 232

7.1.5 Hierarchical models 253

7.2 Data Augmentation 255

7.3 Improper Priors 271

8 Diagnosing Convergence 278

8.1 Stopping the Chain 279

8.2 Monitoring Stationarity Convergence 282

8.3 Monitoring Average Convergence 290

9 Implementation in Missing Data Models 317

9.1 First examples 319

9.2 Finite mixtures of distributions 340

9.3 Extensions 354