Attaining Monotonicity for Bayesian Networks
نویسندگان
چکیده
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 inadvertently 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 practically applied to at least parts of a network.
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