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Comparing Score-Based Methods for Estimating Bayesian Networks Using the Kullback–Leibler Divergence

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  • Jessica Kasza
  • Patty Solomon

Abstract

We recently proposed two methods for estimating Bayesian networks from high-dimensional non-independent and identically distributed data containing exogenous variables and random effects (Kasza et al., 2012). The first method is fully Bayesian, and the second is “residual”-based, accounting for the effects of the exogenous variables by utilizing the notion of restricted maximum likelihood. We describe the methods and compare their performance using the Kullback–Leibler divergence, which provides a natural framework for comparing posterior distributions. In applications where the exogenous variables are not of primary interest, we show that the potential loss of information about parameters of interest is typically small.

Suggested Citation

  • Jessica Kasza & Patty Solomon, 2015. "Comparing Score-Based Methods for Estimating Bayesian Networks Using the Kullback–Leibler Divergence," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(1), pages 135-152, January.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:1:p:135-152
    DOI: 10.1080/03610926.2012.735329
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    Cited by:

    1. Sankaran, P.G. & Sunoj, S.M. & Nair, N. Unnikrishnan, 2016. "Kullback–Leibler divergence: A quantile approach," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 72-79.

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