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Bayesian inference for generalized linear and proportional hazards models via Gibbs sampling. (English) Zbl 0825.62409

Summary: It is shown that Gibbs sampling, making systematic use of an adaptive rejection algorithm proposed by Gilks and Wild, provides a straightforward computational procedure for Bayesian inferences in a wide class of generalized linear and proportional hazards models.

MSC:

62F15 Bayesian inference
62J12 Generalized linear models (logistic models)
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