Bayesian Computation for the Superposition of Nonhomogeneous Poisson Processes
نویسندگان
چکیده
Bayesian inference for the superposition of nonhomogeneous Poisson processes is studied. A Markov chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduce a latent variable that indicates which component of the superposition model gives rise to the failure. This data augmentation approach facilitates speciication of the transitional measure in the Markov chain. The Bayes estimate of the predictive survival function is given. Model selection based on the posterior Bayes factor is studied. A numerical example is given.
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Bayesian Computation for the Superposition ofNonhomogeneous Poisson
Bayesian inference for the superposition of nonhomogeneous Poisson processes is studied. A Markov chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, a latent variable is introduced that indicates which component of the superposition model gives rise to the failure. This data augmentation approach f...
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