Markov Chain Monte Carlo on Test Science Research Document Library https://research.testscience.org/keywords/markov-chain-monte-carlo/ Recent content in Markov Chain Monte Carlo on Test Science Research Document Library Hugo -- 0.129.0 en-us Institute for Defense Analyses Fri, 01 Jan 2016 00:00:00 +0000 Bayesian Reliability- Combining Information https://research.testscience.org/post/2016-bayesian-reliability-combining-information/ Fri, 01 Jan 2016 00:00:00 +0000 https://research.testscience.org/post/2016-bayesian-reliability-combining-information/ One of the most powerful features of Bayesian analyses is the ability to combine multiple sources of information in a principled way to perform inference. This feature can be particularly valuable in assessing the reliability of systems where testing is limited. At their most basic, Bayesian methods for reliability develop informative prior distributions using expert judgment or similar systems. Appropriate models allow the incorporation of many other sources of information, including historical data, information from similar systems, and computer models. One of the most powerful features of Bayesian analyses is the ability to combine multiple sources of information in a principled way to perform inference. This feature can be particularly valuable in assessing the reliability of systems where testing is limited. At their most basic, Bayesian methods for reliability develop informative prior distributions using expert judgment or similar systems. Appropriate models allow the incorporation of many other sources of information, including historical data, information from similar systems, and computer models. We introduce the Bayesian approach to reliability using several examples and point to open problems and areas for future work.

Suggested Citation

Wilson, Alyson G., and Kassandra M. Fronczyk. “Bayesian Reliability: Combining Information.” Quality Engineering, August 26, 2016, 0–0. https://doi.org/10.1080/08982112.2016.1211889.

Paper:

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