Additive bayesian networks for multivariate data: parameter learning, model fitting and applications in veterinary epidemiology

Abstract

Veterinary epidemiology, one of the multifaceted applications of statistics, primarily aims to investigate hypothesized relationships between covariates or predictors of interest and one, or more, outcome variables. Commonly, the biological processes, which generated the data, are extremely complex, resulting in multiple dependencies between explanatory and response variables. Standard epidemiological and statistical approaches have shown a limited ability to sufficiently describe such inter-dependent multivariate connections. The following work extends and improves a methodology that addresses these issues: additive Bayesian networks (ABNs). ABNs are types of graphical model that extend the usual Generalized Linear Model (GLM) to multiple dependent variables through the representation of their joint probability.