Abstract
The properties of automatic model selection are discussed, focusing on PcGets. We explain the background concepts and why automatic methods can perform well. Criticisms of model selection procedures are noted and rebutted. The algorithm is sketched, distinguishing between costs of search and costs of inference: the latter are unavoidable in any statistical science, whereas the costs of searching seem small in comparison. The choice of a ‘search strategy’ and the actual simulation performance of the approach are discussed. We outline a number of developments that will improve the behavior, and generalize the scope, of such programs, and tackle hitherto intractable problems.
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