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A New Scalable Bayesian Network Learning Algorithm with Applications to Economics

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  • Michail Tsagris

    (University of Crete)

Abstract

This paper proposes a new Bayesian network learning algorithm, termed PCHC, that is designed to work with either continuous or categorical data. PCHC is a hybrid algorithm that consists of the skeleton identification phase (learning the relationships among the variables) followed by the scoring phase that assigns the causal directions. Monte Carlo simulations clearly show that PCHC is dramatically faster, enjoys a nice scalability with respect to the sample size, and produces Bayesian networks of similar to, or of higher accuracy than, a competing state of the art hybrid algorithm. PCHC is finally applied to real data illustrating its performance and advantages.

Suggested Citation

  • Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10065-7
    DOI: 10.1007/s10614-020-10065-7
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    Cited by:

    1. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.

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