What Is Markov Chain Monte Carlo? Markov Chain Monte Carlo (MCMC) is a powerful technique used in statistics and various scientific fields to sample from complex probability distributions. It is ...

Monte Carlo methods and Markov Chain algorithms have long been central to computational science, forming the backbone of numerical simulation in a variety of disciplines. These techniques employ ...

The Annals of Applied Probability, Vol. 9, No. 4 (Nov., 1999), pp. 1202-1225 (24 pages) This paper analyzes the performance of importance sampling distributions for computing expectations with respect ...

Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time. Queuing theory, terminology, and single queue systems are studied with ...

Bayesian versions of the classical one-way random effects model are widely used to analyze data. If the standard diffuse prior is adopted, there is a simple block Gibbs sampler that can be employed to ...

Markov Chain Monte Carlo (MCMC) methods allow Bayesian models to be fitted, where prior distributions for the model parameters are specified. By default MLwiN sets diffuse priors which can be used to ...