| PhD Viva

Name of the Speaker: Ms. Shivani Bathla (EE17D037)
Guide: Prof. Vinita Vasudevan
Online meeting link: https://meet.google.com/idm-gkwh-sem
Date/Time: 7th May 2024 (Tuesday), 11:00 AM
Title: Incremental Build-Infer-Approximate (IBIA): A novel framework for approximate inference in probabilistic graphical models

Abstract

Exact inference in probabilistic graphical models (PGM) is intractable and has an exponential dependence on the size of the largest clique in the corresponding clique tree (CT), necessitating approximations. Existing methods for approximate inference either use sampling based methods or use iterative message-passing algorithms, which are slow to converge for many benchmarks.

In the first part of this thesis, we propose a new framework for approximate inference based on an incremental build-infer-approximate (IBIA) paradigm which converts the PGM into a sequence of clique tree forests (SCTF), each with bounded clique sizes. In the second part of this thesis, we show how the problem of reliability analysis in digital circuits can be formulated as that of Bayesian inference. The proposed framework was evaluated using several benchmark sets from recent UAI competitions and large digital benchmark circuits. The results show that our method gives either better or comparable accuracy than the state-of-the-art methods, with competitive runtimes.