A Bayesian Network model for predicting the outcome of in vitro fertilization

Abstract

We present a Bayesian network model for predicting the outcome of in-vitro fertilization (IVF). The problem is characterized by a peculiar missingness process, and we propose a simple but effective averaging approach which improves parameter estimates compared to the traditional MAP estimation. The model can provide relevant insights to IVF experts. 

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