Numerical transient analysis of Markov models
Computers and Operations Research
Acyclic discrete phase type distributions: properties and a parameter estimation algorithm
Performance Evaluation
Queueing Networks and Markov Chains
Queueing Networks and Markov Chains
Closed form solutions for mapping general distributions to quasi-minimal PH distributions
Performance Evaluation - Modelling techniques and tools for computer performance evaluation
A Novel Approach for Phase-Type Fitting with the EM Algorithm
IEEE Transactions on Dependable and Secure Computing
Efficient phase-type fitting with aggregated traffic traces
Performance Evaluation
A minimal representation of Markov arrival processes and a moments matching method
Performance Evaluation
An Algorithm for Computing Minimal% Coxian Representations
INFORMS Journal on Computing
| Hi-index | 0.00 |
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.