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Learning a bayesian network from ordinal data

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  • Flaminia Musella

Abstract

Bayesian networks are graphical models that represent the joint distributionof a set of variables using directed acyclic graphs. When the dependence structure is unknown (or partially known) the network can be learnt from data. In this paper, we propose a constraint-based method to perform Bayesian networks structural learning in presence of ordinal variables. The new procedure, called OPC, represents a variation of the PC algorithm. A nonparametric test, appropriate for ordinal variables, has been used. It will be shown that, in some situation, the OPC algorithm is a solution more efficient than the PC algorithm.

Suggested Citation

  • Flaminia Musella, 2011. "Learning a bayesian network from ordinal data," Departmental Working Papers of Economics - University 'Roma Tre' 0139, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0139
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    More about this item

    Keywords

    Structural Learning; Monotone Association; Nonparametric Methods;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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