Overview of Markov Chain Monte Carlo for Statistical Inference and its Application
Bo. Tao, Chin Teck Chai · 2002
This paper presents an overview of Markov Chain Monte Carlo (MCMC) methods for statistical inference and applications. The article begins by describing ordinary Monte Carlo methods, which in principle has the same goals as the MCMC but can hardly be implemented in practice. Following that basic Markov Chain Monte Carlo is discussed, which is founded on the Hastings algorithm and includes Metropolis method and the Gibbs sampler as special cases. Finally, various special applications of Markov Chain Monte Carlo methods are briefly mentioned and some recent development of MCMC are covered in final remarks section. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.