Attaining monotonicity for Bayesian networks

Authors:
Merel T. Rietbergen;Linda C. Van Der Gaag
Affiliations:
Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands;Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands
Venue:
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Year:
2011

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Abstract

Many real-life Bayesian networks are expected to exhibit commonly known properties of monotonicity, in the sense that higher values for the observable variables should make higher values for the main variable of interest more likely. Yet, violations of these properties may be introduced into a network despite careful engineering efforts. In this paper, we present a method for resolving such violations of monotonicity by varying a single parameter probability. Our method constructs intervals of numerical values to which a parameter can be varied to attain monotonicity without introducing new violations. We argue that our method has a high runtime, yet can be of practical value for specific domains.