Conditional probability generation methods for high reliability effects-based decision making

TL;DR

This paper introduces three novel methods for generating conditional probability tables (CPTs) in Bayesian networks, improving predictive accuracy and reliability especially when only soft evidence is available, validated through case studies.

Contribution

It presents new CPT generation techniques that outperform expert elicited tables, including methods for soft evidence and nonlinear functions, with new quality measures for CPT assessment.

Findings

CPTs generated by new methods show higher predictive reliability.

The methods outperform SME-based CPTs in case studies.

New quality measures effectively evaluate CPT goodness.

Abstract

Decision making is often based on Bayesian networks. The building blocks for Bayesian networks are its conditional probability tables (CPTs). These tables are obtained by parameter estimation methods, or they are elicited from subject matter experts (SME). Some of these knowledge representations are insufficient approximations. Using knowledge fusion of cause and effect observations lead to better predictive decisions. We propose three new methods to generate CPTs, which even work when only soft evidence is provided. The first two are novel ways of mapping conditional expectations to the probability space. The third is a column extraction method, which obtains CPTs from nonlinear functions such as the multinomial logistic regression. Case studies on military effects and burnt forest desertification have demonstrated that so derived CPTs have highly reliable predictive power, including superiority over the CPTs obtained from SMEs. In this context, new quality measures for determining the goodness of a CPT and for comparing CPTs with each other have been introduced. The predictive power and enhanced reliability of decision making based on the novel CPT generation methods presented in this paper have been confirmed and validated within the context of the case studies.