• Home
• Staff & Students
• Vacancies

Sumeetpal Singh

 

University of Cambridge

(http://www-sigproc.eng.cam.ac.uk/~sss40/)

 

Wednesday 29 February 2012

16:00

 

B10 Seminar Room, Basement,

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

 

Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

 

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. Preprint: http://arxiv.org/abs/1103.5399v1