Bayesian Parameter Estimation of AR(1) with Multiple Change Points

  • Chaobing He Anyang Normal University
Keywords: likelihood function, full conditional distribution, MCMC methods, Gibbs sampling, Metropolis-Hastings algorithm

Abstract

This paper mainly studies the parameter estimation of AR(1) with multiple change points by MCMC methods. The full conditional distributions of all parameters are dicussed. The means of Gibbs samples are taken as Bayesian estimations of the parameters. The implementation steps of Gibbs sampling are introduced in detail. Random simulation results show that Bayesian estimations of the parameters are fairly accurate

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Author Biography

Chaobing He, Anyang Normal University

School of Mathematics and Statistics, Anyang Normal University, Anyang 455000, China

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Published
2019-07-30
How to Cite
Chaobing He. (2019). Bayesian Parameter Estimation of AR(1) with Multiple Change Points. EPH - International Journal of Mathematics and Statistics (ISSN: 2208-2212), 5(7), 01-10. Retrieved from https://ephjournal.com/index.php/ms/article/view/1500