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
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