Scoring Bayesian Networks of Mixed Variables

International Journal of Data Science and Analytics
Bryan AndrewsGregory F Cooper

Abstract

In this paper we outline two novel scoring methods for learning Bayesian networks in the presence of both continuous and discrete variables, that is, mixed variables. While much work has been done in the domain of automated Bayesian network learning, few studies have investigated this task in the presence of both continuous and discrete variables while focusing on scalability. Our goal is to provide two novel and scalable scoring functions capable of handling mixed variables. The first method, the Conditional Gaussian (CG) score, provides a highly efficient option. The second method, the Mixed Variable Polynomial (MVP) score, allows for a wider range of modeled relationships, including non-linearity, but it is slower than CG. Both methods calculate log likelihood and degrees of freedom terms, which are incorporated into a Bayesian Information Criterion (BIC) score. Additionally, we introduce a structure prior for efficient learning of large networks and a simplification in scoring the discrete case which performs well empirically. While the core of this work focuses on applications in the search and score paradigm, we also show how the introduced scoring functions may be readily adapted as conditional independence tests for con...Continue Reading

References

Jan 1, 1998·Multivariate Behavioral Research·R ScheinesT Richardson
Jun 15, 2016·BMC Bioinformatics·Andrew J SedgewickPanayiotis V Benos

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Citations

Apr 21, 2020·Wiley Interdisciplinary Reviews. Systems Biology and Medicine·Edwin K SilvermanJan Baumbach
Mar 4, 2021·International Journal of Molecular Sciences·Andrei S RodinPeter P Lee
Mar 12, 2021·Frontiers in Big Data·Luise Gootjes-DreesbachHolger Fröhlich
Mar 12, 2021·Frontiers in Big Data·Nandini Ramanan, Sriraam Natarajan

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