MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||4|
|Section||Computer, Algorithm, Control and Application Engineering|
|Published online||08 March 2016|
Application of Markov chain Monte carlo method in Bayesian statistics
Department of Foundation, Shandong Yingcai University, Jinan 250104, China
a E-mail addresses: firstname.lastname@example.org
In statistical inference methods, bayesian method is a method of great influence. This paper introduces the basic idea of the bayesian method. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical computation problems related to Bayesian inference. Markov chain Monte Carlo method is essentially a Monte Carlo synthesis procedure. The random sample of it is related to a Markov chain. It is a widely used stochastic simulation method. This paper mainly introduces Gibbs sampling, and its application in Bayesian statistics. We use a simple example to illustrate how to tackle a typical Bayesian problem via the MCMC method
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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