title: Modeling Women's Menstrual Cycles using PICI Gates in Bayesian Network.
abstract: A major difficulty in building Bayesian network (BN) models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with this problem is through parametric conditional probability distributions that usually require only a number of parameters that is linear in the number of parents. In this paper, we introduce a new class of parametric models, the Probabilistic Independence of Causal Influences (PICI) models, that aim at lowering the number of parameters required to specify local probability distributions, but are still capable of efficiently modeling a variety of interactions. A subset of PICI models is decomposable and this leads to significantly faster inference as compared to models that cannot be decomposed. We present an application of the proposed method to learning dynamic BNs for modeling a woman's menstrual cycle. We show that PICI models are especially useful for parameter learning from small data sets and lead to higher parameter accuracy than when learning CPTs.
authors.affiliation: Operational and Decision Analysis Group, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, SN6 8LA, United Kingdom.
authors.forename: Adam
authors.initials: A
authors.lastname: Zagorecki
authors.affiliation: Faculty of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland.
authors.forename: Anna
authors.initials: A
authors.lastname: Łupińska-Dubicka
authors.affiliation: Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA.
authors.forename: Mark
authors.initials: M
authors.lastname: Voortman
authors.affiliation: Faculty of Computer Science, Białystok University of Technology, Wiejska 45A, 15-351 Białystok, Poland; Decision Systems Laboratory, School of Information Sciences and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15260, USA.
authors.forename: Marek J
authors.initials: MJ
authors.lastname: Druzdzel
keywords: Bayesian networks; PICI gates; inference; modeling; parameter learning
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