ABSTRACT

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applications, Markov Chain Monte Carlo: Stochastic Simul

chapter |8 pages

Introduction

chapter 1|32 pages

Stochastic simulation

chapter 2|40 pages

Bayesian inference

chapter 3|32 pages

Approximate methods of inference

chapter 4|28 pages

Markov chains

chapter 5|50 pages

Gibbs sampling

chapter 6|46 pages

Metropolis-Hastings algorithms

chapter 7|52 pages

Further topics in MCMC