Skip to main content
Article thumbnail
Location of Repository

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

By Thomas A. Dean, Sumeetpal S. Singh, Ajay Jasra and Gareth W. Peters


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.Comment: First version: 1 October 201

Topics: Mathematics - Statistics Theory, Statistics - Methodology, Primary: 62M09, Secondary: 62B99, 62F12, 65C05
Year: 2011
OAI identifier:
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • (external link)
  • Suggested articles

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.