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