Preface
1 Introduction and Examples
1.1 The Need of Monte Carlo Techniques
1.2 Scope and Outline of the Book
1.3 Computations in Statistical Physics
1.4 Molecular Structure Simulation
1.5 Bioinformatics: Finding Weak Repetitive Patterns
1.6 Nonlinear Dynamic System: Target Tracking
1.7 Hypothesis Testing for Astronomical Observations
1.8 Bayesian Inference of Multilevel Models
1.9 Monte Carlo and Missing Data Problems
2 Basic Principles: Rejection， Weighting， and Others
2.1 Generating Simple Random Variables
2.2 The Rejection Method
2.3 Variance Reduction Methods
2.4 Exact Methods for Chain-Structured Models
2.4.1 Dynamic programming
2.4.2 Exact simulation
2.5 Importance Sampling and Weighted Sample
2.5.1 An example
2.5.2 The basic idea
2.5.3 The "rule of thumb"for importance sampling
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3 Theory of Sequential Monte Carlo
4 Sequential Monte Carlo in Action
5 Metropolis Algorithm and Beyond
6 The Gibbs Sampler
7 Cluster Algorithms for the Ising Model
8 General Conditionl Sampling
9 Molecular Dynamics and Hybrid Monte Carlo
10 Multievel Sampling and Optimization Methods
11 Population-Based Monte Carlo Methods
12 Markov Chains and Their Convergence
13 Selected Theoretical Topics
A Basics in Probability and Statistics
References
Author Index
Subject Index
