Bayesian data mining, with application to benchmarking and credit scoring

The purpose of this article is to show that Bayesian methods, coupled with Markov chain Monte Carlo computational techniques, can be successfully employed in the analysis of highly dimensional complex datasets, such as those occurring in data mining applications. Our methodology employs conditional independence graphs to localize model specification and inferences, thus allowing a considerable gain in flexibility of modelling and efficiency of the computations.