Parameter Estimation for Hidden Markov Models with Intractable Likelihoods

  title={Parameter Estimation for Hidden Markov Models with Intractable Likelihoods},
  author={A. Dean and Sumeetpal S. Singh and 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… CONTINUE READING
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Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications

  • P. Del Moral
  • Springer-Verlag: New York.
  • 2004
Highly Influential
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Semi-automatic approximate Bayesian Computation

  • P. Fearnhead, D. Prangle
  • Technical report, University of Lancaster.
  • 2010
Highly Influential
3 Excerpts

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