Learning Bayesian networks with and without hidden variables
Wednesday 6 May 2020, 2.00PM
Speaker(s): James Cussens
Bayesian networks (BNs) represent patterns of conditional independence between random variables and sometimes also represent causal relations. There are two main approaches to learning BNs from data: "search-and-score" and "constraint-based". I will describe both of these approaches and consider their competing merits. One advantage of the "constraint-based" approach is that it lends itself naturally to detecting when there must be hidden (aka "latent") variables in the data-generating process. I will briefly describe ongoing work where I attempt to learn BNs with hidden variables by learning a set of BNs without them
Special thanks to AI seminar organiser, Felix Ulrich-Oltean
for arranging and recording the seminar.