Learning a bayesian network from ordinal data

Flaminia Musella

No 139, Departmental Working Papers of Economics - University 'Roma Tre' from Department of Economics - University Roma Tre

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.

Keywords: Structural Learning; Monotone Association; Nonparametric Methods (search for similar items in EconPapers)
JEL-codes: C14 C51 (search for similar items in EconPapers)
Date: 2011-10
New Economics Papers: this item is included in nep-ecm and nep-net
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Persistent link: https://EconPapers.repec.org/RePEc:rtr:wpaper:0139

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