A PC algorithm variation for ordinal variables

Flaminia Musella ()

Computational Statistics, 2013, vol. 28, issue 6, 2749-2759

Abstract: Bayesian networks are graphical models that represent the joint distribution of a set of variables using directed acyclic graphs. The graph can be manually built by domain experts according to their knowledge. However, when the dependence structure is unknown (or partially known) the network has to be estimated from data by using suitable learning algorithms. In this paper, we deal with a constraint-based method to perform Bayesian networks structural learning in the presence of ordinal variables. We propose an alternative version of the PC algorithm, which is one of the most known procedures, with the aim to infer the network by accounting for additional information inherent to ordinal data. The proposal is based on a nonparametric test, appropriate for ordinal variables. A comparative study shows that, in some situations, the proposal discussed here is a slightly more efficient solution than the PC algorithm. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Directed acyclic graph; Structural learning; Monotone association; Nonparametric methods (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:6:p:2749-2759

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DOI: 10.1007/s00180-013-0426-5

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