Bayesian Inference for Generalized Linear and Proportional Hazards Models Via Gibbs Sampling

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.

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