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Seminar: An Introduction to Bayesian Network Inference using Variable Elimination

Wed., Mar. 23, 2016 1:30 p.m.

Location: CL 431

Title: An Introduction to Bayesian Network Inference using Variable Elimination

Speaker: Jhonatan Oliveira
Date: March 23 (W)
Time: 1:30pm - 2:20pm 
Room: CL 431

Abstract: Bayesian networks (BNs) are a probabilistic graphical model used for reasoning under uncertainty. Queries can be answered in a BN using a process called inference. Newcomers are often introduced to BN inference with a simple algorithm called Variable Elimination (VE). VE can perform inference by summing out variables and multiplying conditional probability tables (CPTs) from the BN. Moreover, VE can save computation under some conditions by detecting and removing certain variables and their respective CPTs which are considered unnecessary for a given query. In this presentation, we give an introduction to BN inference using VE. We also show some of VE’s advantages and disadvantages.