Bayesian data mining, with application to benchmarking and credit scoring (Q2722292)

The aim of this paper is to show that computational Bayesian methods can be successfully employed in the analysis of data mining applications. In particular, graphical models are used to localize model specification and inferences, thus allowing a considerable gain in flexibility of modelling, efficiency of the computations and interpretability of the inferential results. By employing Markov chain Monte Carlo methods it is provided a simple and efficient way of calculating model scores which allows to perform model selection on the space of all possible decomposable graphical models that describe the association structure of the data at hand.NEWLINENEWLINENEWLINETo illustrate the methodology two applications of current attention in data mining are considered: financial benchmarking and credit scoring. Both constitute real and challenging applications with which to test the proposed Bayesian scoring method. The proposed methodology allows to extract further information, in the form of conditional independence structures, and associated probability scores, that may be very valuable in a data mining context, where the purpose is mainly exploratory.