Computational Statistics

, Volume 16, Issue 1, pp 173–194 | Cite as

Selecting dynamic graphical models with hidden variables from data

  • Beatriz Lacruz
  • Pilar Lasala
  • Alberto Lekuona


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.


Graphical Models Hidden Markov Models Bayesian Learning 


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Copyright information

© Physica-Verlag 2001

Authors and Affiliations

  • Beatriz Lacruz
    • 1
  • Pilar Lasala
    • 1
  • Alberto Lekuona
    • 1
  1. 1.Departamento de Métodos EstadísticosUniversidad de ZaragozaZaragozaSpain

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