DocumentCode :
3378481
Title :
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
Link To Document :
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