Dani Gamerman
(2006年5月10日刊行，Chapman & Hall/CRC［Texts in Statistical Science Series 69］， ISBN:1584885874)
【目次】INTRODUCTION
STOCHASTIC SIMULATION
Introduction
Generation of Discrete Random Quantities
Generation of Continuous Random Quantities
Generation of Random Vectors and Matrices
Resampling Methods
ExercisesBAYESIAN INFERENCE
Introduction
Bayes' Theorem
Conjugate Distributions
Hierarchical Models
Dynamic Models
Spatial Models
Model Comparison
ExercisesAPPROXIMATE METHODS OF INFERENCE
Introduction
Asymptotic Approximations
Approximations by Gaussian Quadrature
Monte Carlo Integration
Methods Based on Stochastic Simulation
ExercisesMARKOV CHAINS
Introduction
Definition and Transition Probabilities
Decomposition of the State Space
Stationary Distributions
Limiting Theorems
Reversible Chains
Continuous State Spaces
Simulation of a Markov Chain
Data Augmentation or Substitution Sampling
ExercisesGIBBS SAMPLING
Introduction
Definition and Properties
Implementation and Optimization
Convergence Diagnostics
Applications
MCMC-Based Software for Bayesian Modeling
Appendix 5.A: BUGS Code for Example 5.7
Appendix 5.B: BUGS Code for Example 5.8
ExercisesMETROPOLIS-HASTINGS ALGORITHMS
Introduction
Definition and Properties
Special Cases
Hybrid Algorithms
Applications
ExercisesFURTHER TOPICS IN MCMC
Introduction
Model Adequacy
Model Choice: MCMC Over Model and Parameter Spaces
Convergence Acceleration
ExercisesReferences
Author Index
Subject Index
