文件名称：An Introduction to Markov Chain Monte Carlo Methods and Their Actuarial Applications
文件大小：483KB
文件格式：PDF
更新时间：2011-12-18 04:51:56
MCMC Markov Monte Carlo
This paper introduces the readers of the Proceedings to an important class of computer based simulation techniques known as Markov Chain Monte Carlo (MCMC) methods. General properties characterizing these methods will be discussed, but the main emphasis will be placed on one MCMC method known as the Gibbs sampler. The Gibbs sampler permits one to simulate realizations from complicated stochastic models in high dimensions by making use of the model's associated full conditional distributions, which will generally have a much simpler and more manageable form. In its most extreme version, the Gibbs sampler reduces the analysis of a complicated multivariate stochastic model to the consideration of that model's associated univariate full conditional distributions.