Bayesian Nonparametric Inference for Nonhomogeneous Poisson Processes
نویسندگان
چکیده
Several classes of nonparametric priors are employed to model the rate of occurrence of failures of the nonhomogeneous Poisson process used in software reliability or in repairable systems. The classes include the gamma process prior, the beta process prior, and the extended gamma process prior. We derive the posterior distribution for each process. Sampling based methods are developed for Bayesian inference. Numerical comparisons among the three classes are performed on a real software failure data set.
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