Additive Bayesian networks for an epidemiological analysis of swine diseases
Marta Pittavino, Reinhard Furrer · Florence Research (University of Florence) · 2018
Additive Bayesian networks (ABNs) are types of graphical models that extend the usual generalized linear model (GLM) to multiple dependent variables through the representation of joint probability distribution. Thanks to their flexible properties, ABNs have been widely used in epidemiological analyses. In this work we present a veterinary case study where ABNs are used to explore multivariate swine diseases data of medical relevance. We then compare the results with a classical methodology. Finally, we highlight the key difference between a multivariable standard (GLM) and a multivariate (ABN) approach: the latter attempts not only to identify statistically associated variables, but also to additionally separate these into those directly and indirectly dependent with one or more outcome variables.