Inference and Predictions from Poisson Point Processes Incorporating Expert Knowledge
作者:
Sylvia Campodónico,
NozerD. Singpurwalla,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 429
页码: 220-226
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476505
出版商: Taylor & Francis Group
关键词: Expert knowledge;Logarithmic-Poisson process;Nonhomogeneous Poisson process;Power-law process;Reliability analysis;Software reliability
数据来源: Taylor
摘要:
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.
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