Bayesian inference for the power law process

Shaul K. Bar-Lev, Idit Lavi, Benjamin Reiser

Research output: Contribution to journalArticlepeer-review

Abstract

The power law process has been used to model reliability growth, software reliability and the failure times of repairable systems. This article reviews and further develops Bayesian inference for such a process. The Bayesian approach provides a unified methodology for dealing with both time and failure truncated data. As well as looking at the posterior densities of the parameters of the power law process, inference for the expected number of failures and the probability of no failures in some given time interval is discussed. Aspects of the prediction problem are examined. The results are illustrated with two data examples.

Original languageEnglish
Pages (from-to)623-639
Number of pages17
JournalAnnals of the Institute of Statistical Mathematics
Volume44
Issue number4
DOIs
StatePublished - Dec 1992

Keywords

  • Bayesian inference
  • Power law process
  • prediction
  • repairable system

ASJC Scopus subject areas

  • Statistics and Probability

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