Notice: COVID-19 resources, information and plans for current and upcoming academic terms. Learn more.

Subscribe by RSS Subscribe by RSS

Seminar: On Testing Independencies in Bayesian Networks

Mon., Mar. 21, 2016 1:30 p.m.

Location: CL 431

Title: On Testing Independencies in Bayesian Networks

Speaker: André dos Santos
Date: March 21 (M)
Time: 1:30pm - 2:20pm 
Room: CL 431

Abstract: Testing independencies in Bayesian networks (BNs) is an fundamental task in probabilistic reasoning. It can reveal the conditional independence relations implied by the directed acyclic graph (DAG) of a BN. One method often utilized for this task is d-separation. Although d-separation has linear time complexity, many have had difficulties in understanding its inner workings. m-Separation is an equivalent method for testing independencies in BNs which coverts the problem into classical separation in undirected graphs. In this seminar we explore the key features of d-separation and m-separation. We show the main advantages and disadvantages of using d-separation and m-separation for testing independencies in BNs.