Usefulness of Bayesian networks in epidemiological studies

Medicina Balear 2014; 29 (3): 10-17 Bayesian networks (BNs)16, 25 also referred to as causal networks or beliefs networks, are a form of statistical modelling which allow us to obtain a graphical network describing the dependencies and conditional independencies from empirical data. They have proven to be a promising tool for discovering relationships9, they capture the way an expert understands the relationships among all the features 6 and, even, they have been used in data analysis 8. The origins of BN modelling lie within the data mining and machine learning literature5, 13. BNs are a kind of probabilistic graphical model (PGM)18, which combine graph theory (to help in the representation and resolution of complex problems) and probability theory (as a way of representing uncertainty). A PGM is defined as a graph where nodes represent random variables4, 12 and arcs represent dependencies between such variables11, 24. A PGM is called a BN when the graph connecting its variables is a directed acyclic graph (DAG). The graphical representation of BNs captures the compositional structure of the relations and the general aspects of all probability distributions factorized according to that structure12. eISSN 2255-0569