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Bayesian Computations for Reliability Analysis in Dynamic Environments

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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 this chapter, we consider systems operating under a dynamic environment that causes changes in the failure characteristics of the system. We discuss different modeling strategies to describe the evolution of the dynamic environment and develop Bayesian analysis of the models using Markov Chain Monte Carlo methods and data augmentation techniques. We present illustrations from repairable systems using data from software testing, railroad track maintenance, and power outages.

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Author information

Authors and Affiliations

  1. George Washington University, Washington, DC, USA

    Atilla Ay & Refik Soyer

Authors
  1. Atilla Ay
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  2. Refik Soyer
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Corresponding author

Correspondence to Refik Soyer .

Editor information

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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Ay, A., Soyer, R. (2022). Bayesian Computations for Reliability Analysis in Dynamic Environments. 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_5

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