Knowledge Intensive Learning of Credal Networks

Saurabh Mathur, Alessandro Antonucci, Sriraam Natarajan

Keywords: Probabilistic Graphical Models, Bayesian Networks, Credal Networks, Knowledge-guided learning

TL;DR: We propose an algorithm to learn sets of probability distributions from data using domain-specific qualitative knowledge.

Abstract: Bayesian networks are a popular class of directed probabilistic graphical models that allow for closed-form learning of the local parameters if complete data are available. However, learning the parameters is challenging when the data are sparse, incomplete, and uncertain. In this work, we present an approach to this problem based on credal networks, a generalization of Bayesian networks based on set-valued local parameters. We derive an algorithm to learn such set-valued parameters from data using qualitative knowledge in the form of monotonic influence statements. Our empirical evaluation shows that using qualitative knowledge reduces uncertainty about the parameters without significant loss in accuracy.

List Of Authors: Mathur, Saurabh and Antonucci, Alessandro and Natarajan, Sriraam

Code Url: https://github.com/saurabhmathur96/credal-cpd/

Submission Number: 534