Exploring Graphical Models with Bayesian Learning and MCMC for Failure Diagnosis

Abstract

Graphical models are powerful machine learning techniques for data analytics. Being capable of statistical reasoning and probabilistic inference, graphical models have the advantages of encoding prior knowledges into the learning procedure, and producing explainable models that can be understood and effectively tuned. In this work, we describe our exploration on the frontier of using graphical models for improving circuit diagnosis results. A statistical framework has been proposed for this aim, which builds Bayesian inference models using directed chain graphs, and structural learning models using undirected tree graphs. As a generative model, the framework integrates Markov chain Monte Carlo (MCMC) algorithm for sampling to evaluate the quality of diagnostic results. It exploits maximum-likelihood to estimate the underlying defect types, which can be informative towards the possible follow-up failure analysis. Five circuit examples demonstrate that the proposed framework achieves the same or better results over a state-of-the-art work. Moreover, our method also shows opportunities for dealing with missing features and locating root causes.