Bayesian analysis of Markov Modulated Bernoulli Processes

We consider Markov Modulated Bernoulli Processes (MMBP) where the success probability of a Bernoulli process evolves over time according to a Markov chain. The MMBP is applied in reliability modeling where systems and components function in a randomly changing environment. Some of these applications...

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Bibliographic Details
Published in:Mathematical methods of operations research (Heidelberg, Germany) Germany), 2003-04, Vol.57 (1), p.125-140
Main Authors: ÖZEKICI, S, SOYER, R
Format: Article
Language:English
Subjects:
Applied sciences
Electrical engineering. Electrical power engineering
Electrical power engineering
Exact sciences and technology
Markov processes
Mathematics
Operation. Load control. Reliability
Operational research and scientific management
Operational research. Management science
Power networks and lines
Probability and statistics
Probability theory and stochastic processes
Reliability theory. Replacement problems
Sciences and techniques of general use
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Summary:We consider Markov Modulated Bernoulli Processes (MMBP) where the success probability of a Bernoulli process evolves over time according to a Markov chain. The MMBP is applied in reliability modeling where systems and components function in a randomly changing environment. Some of these applications include, but are not limited to, reliability assessment in power systems that are subject to fluctuating weather conditions over time and reliability growth processes that are subject to design changes over time. We develop a general setup for analysis of MMBPs with a focus on reliability modeling and present Bayesian analysis of failure data and illustrate how reliability predictions can be obtained.
ISSN:1432-2994
1432-5217
DOI:10.1007/s001860200268