Abstract.
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
(Manuscript received: May 2002/Final version received: October 2002)
Acknowledgement. This research is supported by subcontract #35352-6085 with Cornell University under WO833304 from the Electric Power Research Institute and US Army Research Office.
Rights and permissions
About this article
Cite this article
Özekici, S., Soyer, R. Bayesian analysis of Markov Modulated Bernoulli Processes. Mathematical Methods of OR 57, 125–140 (2003). https://doi.org/10.1007/s001860200268
Issue Date:
DOI: https://doi.org/10.1007/s001860200268