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Modeling women's menstrual cycles using PICI gates in Bayesian network
International Journal of Approximate Reasoning ( IF 3.9 ) Pub Date : 2016-03-01 , DOI: 10.1016/j.ijar.2015.12.002 Adam Zagorecki 1 , Anna Łupińska-Dubicka 2 , Mark Voortman 3 , Marek J Druzdzel 4
International Journal of Approximate Reasoning ( IF 3.9 ) Pub Date : 2016-03-01 , DOI: 10.1016/j.ijar.2015.12.002 Adam Zagorecki 1 , Anna Łupińska-Dubicka 2 , Mark Voortman 3 , Marek J Druzdzel 4
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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.
中文翻译:
使用贝叶斯网络中的 PCI 门模拟女性的月经周期
构建贝叶斯网络 (BN) 模型的一个主要困难是条件概率表的大小,它随着父节点的数量呈指数增长。解决这个问题的一种方法是通过参数化条件概率分布,通常只需要一些参数在本文中,我们引入了一类新的参数模型,即因果影响的概率独立性 (PICI) 模型,旨在减少指定局部概率分布所需的参数数量,但仍然是 PCI 模型的一个子集是可分解的和与无法分解的模型相比,这导致推理速度明显加快。我们将所提出的方法应用于学习动态 BN 以模拟女性月经周期。我们表明,PICI 模型对于从小数据集学习参数特别有用,并且比学习 CPT 时参数准确度更高。
更新日期:2016-03-01
中文翻译:
使用贝叶斯网络中的 PCI 门模拟女性的月经周期
构建贝叶斯网络 (BN) 模型的一个主要困难是条件概率表的大小,它随着父节点的数量呈指数增长。解决这个问题的一种方法是通过参数化条件概率分布,通常只需要一些参数在本文中,我们引入了一类新的参数模型,即因果影响的概率独立性 (PICI) 模型,旨在减少指定局部概率分布所需的参数数量,但仍然是 PCI 模型的一个子集是可分解的和与无法分解的模型相比,这导致推理速度明显加快。我们将所提出的方法应用于学习动态 BN 以模拟女性月经周期。我们表明,PICI 模型对于从小数据集学习参数特别有用,并且比学习 CPT 时参数准确度更高。