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Modeling women's menstrual cycles using PICI gates in Bayesian network

Zagorecki, A and Łupińska-Dubicka, A and Voortman, M and Druzdzel, MJ (2016) Modeling women's menstrual cycles using PICI gates in Bayesian network. International Journal of Approximate Reasoning, 70. 123 - 136. ISSN 0888-613X

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Zagorecki, A
Łupińska-Dubicka, A
Voortman, M
Druzdzel, MJmarek@sis.pitt.eduDRUZDZEL0000-0002-7598-2286
Date: 1 March 2016
Date Type: Publication
Journal or Publication Title: International Journal of Approximate Reasoning
Volume: 70
Page Range: 123 - 136
DOI or Unique Handle: 10.1016/j.ijar.2015.12.002
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0888-613X
PubMed Central ID: PMC4727251
PubMed ID: 26834313
Date Deposited: 19 Jul 2016 13:02
Last Modified: 30 Mar 2021 13:55


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