Unity Smoothing for Handling Inconsistent Evidence in Bayesian Networks and Unity Propagation for Faster Inference

TL;DR

This paper introduces Unity Smoothing (US) for better handling of inconsistent evidence in Bayesian networks, matching Laplace smoothing in accuracy but using less memory, and Unity Propagation (UP) for faster inference in junction tree algorithms.

Contribution

The paper presents Unity Smoothing as a novel method for managing inconsistencies in Bayesian networks and introduces Unity Propagation for accelerating junction tree inference.

Findings

US achieves comparable accuracy to Laplace smoothing.

US uses less memory in sparse data applications.

UP significantly speeds up junction tree inference.

Abstract

We propose Unity Smoothing (US) for handling inconsistencies between a Bayesian network model and new unseen observations. We show that prediction accuracy, using the junction tree algorithm with US is comparable to that of Laplace smoothing. Moreover, in applications were sparsity of the data structures is utilized, US outperforms Laplace smoothing in terms of memory usage. Furthermore, we detail how to avoid redundant calculations that must otherwise be performed during the message passing scheme in the junction tree algorithm which we refer to as Unity Propagation (UP). Experimental results shows that it is always faster to exploit UP on top of the Lauritzen-Spigelhalter message passing scheme for the junction tree algorithm.