Practical Guidelines for Learning Bayesian Networks from Small Data Sets

Abstract

Model learning is the process of extracting, analysing and synthesising information from data sets. Graphical models are a suitable framework for probabilistic modelling. A Bayesian Network (BN) is a probabilistic graphical model, which represents joint distributions in an intuitive and efficient way. It encodes the probability density (or mass) function of a set of variables by specifying a number of conditional independence statements in the form of a directed acyclic graph. Specifying the structure of the model is one of the most important design choices in graphical modelling. Notwithstanding their potential, there are several limitations to learning BNs from small data sets. In this paper, we introduce a set of practical guidelines a modeller can use to deal with these limitations. The main goal of the guidelines is to increase awareness of the underlying assumptions and the tacit implications of several learning techniques. Unsurprisingly, one of the authors’ findings is that learning BNs from small data sets is a complex and challenging task, yet potentially very rewarding. The paper also draws attention to the amount of subjective input needed from the modeller and the necessity to tailor solutions on the particularity of the application.