Gradient Free Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
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DOI: 10.1007/s11009-013-9357-4
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- Pierre Del Moral & Arnaud Doucet & Ajay Jasra, 2006. "Sequential Monte Carlo samplers," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 411-436, June.
- McKinley Trevelyan & Cook Alex R & Deardon Robert, 2009. "Inference in Epidemic Models without Likelihoods," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-40, July.
- George Poyiadjis & Arnaud Doucet & Sumeetpal S. Singh, 2011. "Particle approximations of the score and observed information matrix in state space models with application to parameter estimation," Biometrika, Biometrika Trust, vol. 98(1), pages 65-80.
- Veronika Czellar, 2012. "Tracking Beliefs: Accurate Methods for Approximate Bayesian Computation Filtering," Post-Print hal-00713248, HAL.
- Pitt, Michael K., 2002. "Smooth particle filters for likelihood evaluation and maximisation," Economic Research Papers 269464, University of Warwick - Department of Economics.
- Pitt, Michael K, 2002. "Smooth Particle Filters for Likelihood Evaluation and Maximisation," The Warwick Economics Research Paper Series (TWERPS) 651, University of Warwick, Department of Economics.
- repec:dau:papers:123456789/5724 is not listed on IDEAS
- Tadic, Vladislav B. & Doucet, Arnaud, 2005. "Exponential forgetting and geometric ergodicity for optimal filtering in general state-space models," Stochastic Processes and their Applications, Elsevier, vol. 115(8), pages 1408-1436, August.
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Cited by:
- Johan Dahlin & Mattias Villani & Thomas B. Schon, 2015. "Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods," Papers 1506.06975, arXiv.org, revised Jun 2017.
- Patrick L. McDermott & Christopher K. Wikle & Joshua Millspaugh, 2017. "Hierarchical Nonlinear Spatio-temporal Agent-Based Models for Collective Animal Movement," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 294-312, September.
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Keywords
Approximate Bayesian computation; Hidden Markov models; Parameter estimation; Sequential Monte Carlo;All these keywords.
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