An efficient MCMC algorithm for continuous PH distributions

Author

Watanabe, Ryuji ; Okamura, Hiroyuki ; Dohi, Tadashi

Author_Institution

Dept. of Inf. Eng., Hiroshima Univ., Higashi-Hiroshima, Japan

fYear

2012

fDate

9-12 Dec. 2012

Firstpage

1

Lastpage

12

Abstract

This paper proposes an MCMC (Markov chain Monte Carlo) algorithm for estimating continuous phase-type distributions (CPHs). In Bayes estimation, it is well known that MCMC is one of the most useful and practical methods. The concrete MCMC algorithm for CPHs was developed by using Markov jump processes by Bladt et al. (2003). However, the existing MCMC algorithm spends much computation time in some cases. In this paper, we propose a new sampling algorithm which is based on uniformization technique and backward likelihood computation. The proposed algorithm is easier to implement and is more efficient in terms of computation time than the existing method.

Keywords

Bayes methods; Markov processes; Monte Carlo methods; computational complexity; sampling methods; Bayes estimation; CPH; Markov chain Monte Carlo algorithm; Markov jump processes; backward likelihood computation; computation time; concrete MCMC algorithm; continuous phase-type distributions; sampling algorithm; uniformization technique; Computational modeling; Estimation; Markov processes; Proposals; Transient analysis; Uncertainty; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Simulation Conference (WSC), Proceedings of the 2012 Winter

Conference_Location

Berlin

ISSN

0891-7736

Print_ISBN

978-1-4673-4779-2

Electronic_ISBN

0891-7736

Type

conf

DOI

10.1109/WSC.2012.6465313

Filename

6465313