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Improving Discretization Exploiting Dependence Structure

Author

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  • Daniela Marella
  • Mauro Mezzini
  • Paola Vicard

Abstract

Bayesian networks are multivariate statistical models using a directed acyclic graph to represent statistical dependencies among variables. When dealing with Bayesian Networks it is common to assume that all the variables are discrete. This is not often the case in many real contexts where also continuous variables are observed. A common solution consists in discretizing the continuous variables. In this paper we propose a discretization algorithm based on the Kullback-Leibler divergence measure. Formally, we deal with the problem of discretizing a continuous variable Y conditionally on its parents. We show that such a problem is polynomially solvable. A simulation study is finally performed.

Suggested Citation

  • Daniela Marella & Mauro Mezzini & Paola Vicard, 2015. "Improving Discretization Exploiting Dependence Structure," Departmental Working Papers of Economics - University 'Roma Tre' 0199, Department of Economics - University Roma Tre.
    • Handle: RePEc:rtr:wpaper:0199
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    Keywords

    Discretization; Kullback-Leibler divergence measure; Bayesian Networks;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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