Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data. In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing.
References in zbMATH (referenced in 1 article )
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- Shah, Abhik; Woolf, Peter: Python environment for Bayesian learning: inferring the structure of Bayesian networks from knowledge and data (2009)