PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Parameter estimation in pair hidden Markov models.
Elisabeth Gassiat, Ana Arribas-Gil and Catherine Matias
Scandinavian Journal of Statistics 2005.

Abstract

This paper deals with parameter estimation in pair hidden Markov models (pair-HMMs). We first provide a rigorous formalism for these models and discuss possible definitions of likelihoods. The model being biologically motivated, some restrictions with respect to the full parameter space naturally occur. Existence of two different Information divergence rates is established and divergence property (namely positivity at values different from the true one) is shown under additional assumptions. This yields consistency for the parameter in parametrization schemes for which the divergence property holds. Simulations illustrate different cases which are not covered by our results.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:1722
Deposited By:Elisabeth Gassiat
Deposited On:28 November 2005