Conditional probability generation methods for high reliability effects-based decision making
This work addresses the need for more accurate and reliable decision-making tools in domains like military effects and environmental management, though it appears incremental as it builds on existing CPT generation approaches.
The paper tackled the problem of generating conditional probability tables (CPTs) for Bayesian networks, which are often approximated insufficiently, by proposing three new methods that work with soft evidence and nonlinear functions, resulting in CPTs with highly reliable predictive power and superiority over expert-derived ones in military and environmental case studies.
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.