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Parameter estimation for Hidden Markov Models with intractable likelihoods

Dean, TA and Singh, SS and Jasra, A and Peters, GW (2010) Parameter estimation for Hidden Markov Models with intractable likelihoods. Technical Report. CUED, Cambridge.

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Abstract

Approximate Bayesian computation (ABC) is a popular technique for approximating likelihoods and is often used in parameter estimation when the likelihood functions are analytically intractable. Although the use of ABC is widespread in many fields, there has been little investigation of the theoretical properties of the resulting estimators. In this paper we give a theoretical analysis of the asymptotic properties of ABC based maximum likelihood parameter estimation for hidden Markov models. In particular, we derive results analogous to those of consistency and asymptotic normality for standard maximum likelihood estimation. We also discuss how Sequential Monte Carlo methods provide a natural method for implementing likelihood based ABC procedures.

Item Type: Monograph (Technical Report)
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:47
Last Modified: 02 Jun 2014 01:14
DOI:

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