Probabilistic Inference Using Markov Chain Monte Carlo Methods(pdf 144)英文 Contents 1. Introduction 1 2. Probabilistic Inference for Articial Intelligence 2.1 Probabilistic inference with a fully-specied model 2.2 Statistical inference for model parameters 2.3 Bayesian model comparison 2.4 Statistical physics 3. Background on the Problem and its Solution 3.1 Denition of the problem 3.2 Approaches to solving the problem 3.3 Theory of Markov chains 4. The Metropolis and Gibbs Sampling Algorithms 47 4.1 Gibbs sampling 4.2 The Metropolis algorithm 4.3 Variations on the Metropolis algorithm 4.4 Analysis of the Metropolis and Gibbs sampling algorithms 5. The Dynamical and Hybrid Monte Carlo Methods 5.1 The stochastic dynamics method 5.2 The hybrid Monte Carlo algorithm 5.3 Other dynamical methods 5.4 Analysis of the hybrid Monte Carlo algorithm 6. Extensions and Renements 87 6.1 Simulated annealing 6.2 Free energy estimation 6.3 Error assessment and reduction 6.4 Parallel implementation 7. Directions for Research 116 7.1 Improvements in the algorithms 7.2 Scope for applications 8. Annotated Bibliography