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Bayesian Analysis of Stochastic Processes in Reliability

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Bayesian Inference and Computation in Reliability and Survival Analysis

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

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Abstract

In reliability, models involving complex stochastic processes play an important role. This type of model allows analysts to handle many problems such as the missing data or uncertain data problem. The Bayesian approach relying on prior belief or expertise appears to be a natural tool in such situations. Thus the Bayesian approach provides efficient methods for reliability analysis with stochastic processes. The objective of this chapter is to describe the main techniques to make Bayesian inference for stochastic processes.

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Authors and Affiliations

  1. Université Bretagne Sud, LMBA, Vannes, France

    Evans Gouno

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  1. Evans Gouno
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Correspondence to Evans Gouno .

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Editors and Affiliations

  1. Department of Mathematical Sciences, University of South Dakota, Vermillion, SD, USA

    Yuhlong Lio

  2. College of Health Solutions, Arizona State University, Phoenix, AZ, USA

    Ding-Geng Chen

  3. Department of Mathematical Sciences, Bentley University, Waltham, MA, USA

    Hon Keung Tony Ng

  4. Department of Statistics, Tamkang University, New Taipei City, Taiwan

    Tzong-Ru Tsai

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Gouno, E. (2022). Bayesian Analysis of Stochastic Processes in Reliability. In: Lio, Y., Chen, DG., Ng, H.K.T., Tsai, TR. (eds) Bayesian Inference and Computation in Reliability and Survival Analysis. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-88658-5_6

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