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Selecting dynamic graphical models with hidden variables from data

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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.

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Authors and Affiliations

  1. Departamento de Métodos Estadísticos, Universidad de Zaragoza, Edificio de Matemáticas, 3 planta, 50009, Zaragoza, Spain

    Beatriz Lacruz, Pilar Lasala & Alberto Lekuona

Authors
  1. Beatriz Lacruz
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  2. Pilar Lasala
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  3. Alberto Lekuona
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Lacruz, B., Lasala, P. & Lekuona, A. Selecting dynamic graphical models with hidden variables from data. Computational Statistics 16, 173–194 (2001). https://doi.org/10.1007/s001800100058

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