Quasi-Bayes procedures for unsupervised learning

Unsupervised Bayes sequential learning procedures for classification and estimation are often useless in practice because of computational constraints. In this paper, a quasi-Bayes approach is motivated, and discussed in detail for some versions of a two-class decision problem. The proposed procedure mimics closely the formal Bayes solution, whilst involving only a minimal amount of computation. Some numerical illustrations are provided, and the approach is compared with a number of other proposed learning procedures.