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.

Predicting the evolution of clinical skin aging in a multi‐ethnic population: Developing causal Bayesian networks using dermatological expertise

“…The Independence of Causal Influence (ICI) approach offers a solution to the complexity of establishing a direct link between all causal nodes and the effect node 16–19 . In the ICI structure, an independent “impact” node is associated with each risk factor.…”

“…The Independence of Causal Influence (ICI) approach offers a solution to the complexity of establishing a direct link between all causal nodes and the effect node. 16 , 17 , 18 , 19 In the ICI structure, an independent “impact” node is associated with each risk factor. This node captures the local effect of the risk factor on the prediction, specifically the influence when all other causes are in their neutral state.…”