Chapter.1   About R objects, probability distributions, statistics and further topics
Chapter.2   Uniform and non-uniform variate generation, including accept-reject
Chapter.3   Monte Carlo integration, including importance sampling
Chapter.4   Convergence control, effective sample size and acceleration techniques   
Chapter.5   Stochastic optimization, stochastic approximation and the EM algorithm
Chapter.6   Metropolis-Hastings algorithms, including calibration of the acceptance
Chapter.7   Gibbs sampling, latent variables, slice sampling, hierarchical models, and improper priors
Chapter.8   Convergence monitoring for MCMC methods, package coda, without amcmc
