Event: STOR-i Seminar
Speaker: Ingrid Hobæk Haff (http://www.mn.uio.no/math/personer/vit/ingrihaf/)
Venue: A54 Lecture Theatre, PSC
Date: Tuesday 25th April 2017
Title: Structure learning in Bayesian Networks using regular vines
Learning the structure of a Bayesian Network from multidimensional data is an important task in many situations, as it allows understanding conditional(in)dependence relations which in turn can be used for prediction. Current methods mostly assume a multivariate normal or a discrete multinomial model. We propose a new greedy learning algorithm for continuous non-Gaussian variables,where marginal distributions can be arbitrary, as well as the dependency structure. It exploits the regular vine approximation of the model, which is a tree-based hierarchical construction with pair-copulae as building blocks. We show that the networks obtainable with our algorithm belong to a certain subclass of chordal graphs. We illustrate through several examples and real data applications that the possibility of using non-Gaussian margins and anon-linear dependency structure outweighs the restriction to chordal graphs.