ABSTRACT
Model selection methods from the Bayesian perspective rely on full probability specification for each model under consideration. This includes the likelihood and the prior for each model. Beyond this, some methods employ the notions of prior and posterior probabilities on the models. Others omit this and rely on optimizing a criterion defined for each model. When the number of models under consideration is moderate, complete calculations of posterior probabilities or the selection criteria are feasible. For a large set of models, as is often the case in variable selection, various forms of stochastic searches can be quite effective. In this chapter we describe these three types of methods based on: (i) model probabilities, (ii) defined criterion, and (iii) stochastic search, and their use in survival analysis.
