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Selecting dynamic graphical models with hidden variables from data

Author

Listed:
  • Beatriz Lacruz

    (Universidad de Zaragoza)

  • Pilar Lasala

    (Universidad de Zaragoza)

  • Alberto Lekuona

    (Universidad de Zaragoza)

Abstract

Summary Selecting graphical models for a set of variables from data consists of finding the graphical structure and its associated probability distribution which best fit the data. In this paper we propose a new method for selecting Markovian dynamic graphical models from data and, in particular, we develop a new Bayesian technique for selecting graphical hidden Markov models, depicted by a chain graph, from an incomplete data set where values corresponding to hidden or latent variables are not present in data. The proposed method is illustrated by a case study.

Suggested Citation

  • Beatriz Lacruz & Pilar Lasala & Alberto Lekuona, 2001. "Selecting dynamic graphical models with hidden variables from data," Computational Statistics, Springer, vol. 16(1), pages 173-194, March.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:1:d:10.1007_s001800100058
    DOI: 10.1007/s001800100058
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    References listed on IDEAS

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    1. Lacruz, Beatriz & Lasala, Pilar & Lekuona, Alberto, 2000. "Dynamic graphical models and nonhomogeneous hidden Markov models," Statistics & Probability Letters, Elsevier, vol. 49(4), pages 377-385, October.
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    Cited by:

    1. Dirk Temme, 2006. "Constraint-based inference algorithms for structural models with latent confounders— empirical application and simulations," Computational Statistics, Springer, vol. 21(1), pages 151-182, March.

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