Selecting dynamic graphical models with hidden variables from data

Beatriz Lacruz, Pilar Lasala and Alberto Lekuona
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Beatriz Lacruz: Universidad de Zaragoza
Pilar Lasala: Universidad de Zaragoza
Alberto Lekuona: Universidad de Zaragoza

Computational Statistics, 2001, vol. 16, issue 1, No 10, 173-194

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.

Keywords: Graphical Models; Hidden Markov Models; Bayesian Learning (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1007/s001800100058

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