CUED Publications database

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

Dean, TA and Singh, SS and Jasra, A and Peters, GW (2014) Parameter Estimation for Hidden Markov Models with Intractable Likelihoods. Scandinavian Journal of Statistics. n/a-n/a. ISSN 1467-9469

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Abstract

Approximate Bayesian computation (ABC) is a popular technique for analysing data for complex models where the likelihood function is intractable. It involves using simulation from the model to approximate the likelihood, with this approximate likelihood then being used to construct an approximate posterior. In this paper, we consider methods that estimate the parameters by maximizing the approximate likelihood used in ABC. We give a theoretical analysis of the asymptotic properties of the resulting estimator. 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 our likelihood-based ABC procedures.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:47
Last Modified: 08 Dec 2014 02:20
DOI: 10.1111/sjos.12077