Bayesian inference for the power law process
type: Item
instance of: scholarly article
title: Bayesian inference for the power law process
zbMATH Open document ID: 0773.62020
DOI: 10.1007/BF00053394
author: Shaul K. Bar-Lev
author: Idit Lavi
author: Benjamin Reiser
published in: Annals of the Institute of Statistical Mathematics
publication date: 1993-08-25T00:00:00Z
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62N05
zbMATH DE Number: 370605
zbMATH Keywords: time truncated data
zbMATH Keywords: nonhomogeneous Poisson process
zbMATH Keywords: power law process
zbMATH Keywords: reliability growth
zbMATH Keywords: software reliability
zbMATH Keywords: failure times of repairable systems
zbMATH Keywords: unified methodology
zbMATH Keywords: failure truncated data
zbMATH Keywords: posterior densities
zbMATH Keywords: expected number of failures
zbMATH Keywords: probability of no failures
zbMATH Keywords: prediction
Wikidata QID: Q57090332
MaRDI profile type: Publication
cites work: Q3332127
cites work: Q3853012
cites work: Q3214142
cites work: Confidence Interval Procedures for the Weibull Process with Applications to Reliability Growth
cites work: Prediction Intervals for the Weibull Process
cites work: On the Mean Time between Failures for Repairable Systems
cites work: Q4275389
cites work: Bayes inference for a non-homogeneous Poisson process with power intensity law (reliability)
cites work: Some Results on Inference for the Weibull Process
cites work: Inference on a future reliability parameter with the weibull process model
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description: scientific article; zbMATH DE number 370605