Find in Library

Bayesian networks are a type of probabilistic graphical modelslie at the intersection between statistics and machine learning.They have been shown to be powerful tools to encode dependence relationshipsamong the variables of a domain under uncertainty.Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables,as well as temporal processes.In this paper we review Bayesian networks and how they can be learnedautomatically from data by means of structure learning algorithms.Also, we examine how a user can take advantage of these networks for reasoning byexact or approximate inference algorithms that propagate the givenevidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivityanalysis from neuroimaging data. Here we survey key research inneuroscience where Bayesian networks have been used with differentaims: discover associations between variables, perform probabilisticreasoning over the model, and classify new observations with andwithout supervision. The networks are learned from data of any kind--morphological, electrophysiological, -omics andneuroimaging--, thereby broadening the scope --molecular, cellular, structural, functional, cognitiveand medical-- of the brain aspects to be studied.