Inference and predictions from Poisson point processes incorporating expert knowledge

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

30 Scopus Citations
View graph of relations

Author(s)

  • Sylvia Campodónico
  • Nozer D. Singpurwalla

Detail(s)

Original languageEnglish
Pages (from-to)220-226
Journal / PublicationJournal of the American Statistical Association
Volume90
Issue number429
Publication statusPublished - Mar 1995
Externally publishedYes

Abstract

We present a Bayesian approach for inference and predictions from nonhomogeneous Poisson point processes. The novel feature of our approach is the use of “expert knowledge” or “engineering information” on the mean value function of the process. We describe two scenarios from the field of reliability in which engineering information on the mean value function is available. The first scenario pertains to the prediction of software failures during the debugging phase. Here expert knowledge is provided by the published empirical experiences of software engineers involved with the testing and debugging of several software systems. The second scenario pertains to the prediction of defects in a rail segment for which expert knowledge is supplied by an engineering model. © 1995 Taylor & Francis Group, LLC.

Research Area(s)

  • Expert knowledge, Logarithmic-Poisson process, Nonhomogeneous Poisson process, Power-law process, Reliability analysis, Software reliability

Citation Format(s)

Inference and predictions from Poisson point processes incorporating expert knowledge. / Campodónico, Sylvia; Singpurwalla, Nozer D.
In: Journal of the American Statistical Association, Vol. 90, No. 429, 03.1995, p. 220-226.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review