Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data

TL;DR

This paper introduces a non-iterative, efficient algorithm for learning Bayesian network parameters from incomplete data, outperforming traditional methods like EM in speed and accuracy without requiring inference.

Contribution

The paper presents a novel, closed-form, non-iterative algorithm for Bayesian network parameter learning from incomplete data, avoiding inference and improving efficiency.

Findings

Significantly faster than EM in empirical tests

Provides consistent estimates for various missing data mechanisms

Achieves higher accuracy with sufficient data

Abstract

We propose an efficient family of algorithms to learn the parameters of a Bayesian network from incomplete data. In contrast to textbook approaches such as EM and the gradient method, our approach is non-iterative, yields closed form parameter estimates, and eliminates the need for inference in a Bayesian network. Our approach provides consistent parameter estimates for missing data problems that are MCAR, MAR, and in some cases, MNAR. Empirically, our approach is orders of magnitude faster than EM (as our approach requires no inference). Given sufficient data, we learn parameters that can be orders of magnitude more accurate.