Approximate Bayesian Computational methods
Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.
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|Date of creation:||2012|
|Date of revision:|
|Publication status:||Published in Statistics and Computing, 2012, Vol. 22, no. 6. pp. 1167-1180.Length: 13 pages|
|Contact details of provider:|| Web page: http://www.dauphine.fr/en/welcome.html|
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- Laurent E. Calvet & Veronika Czellar, 2011.
"State-Observation Sampling and the Econometrics of Learning Models,"
- Calvet, Laurent-Emmanuel & Czellar , Veronika, 2011. "state-observation sampling and the econometrics of learning models," Les Cahiers de Recherche 947, HEC Paris.
- Chen, Carla & Mengersen, Kerrie & Robert, Christian P., 2010. "Model choice versus model criticism," Economics Papers from University Paris Dauphine 123456789/3514, Paris Dauphine University.
- 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.
- N. Friel & A. N. Pettitt, 2008. "Marginal likelihood estimation via power posteriors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 589-607.
- Joyce Paul & Marjoram Paul, 2008. "Approximately Sufficient Statistics and Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-18, August.
- Francesco Bartolucci & Luisa Scaccia & Antonietta Mira, 2006. "Efficient Bayes factor estimation from the reversible jump output," Biometrika, Biometrika Trust, vol. 93(1), pages 41-52, March.
- 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.
- Gourieroux, C & Monfort, A & Renault, E, 1993.
Journal of Applied Econometrics,
John Wiley & Sons, Ltd., vol. 8(S), pages S85-118, Suppl. De.
- J. M�ller & A. N. Pettitt & R. Reeves & K. K. Berthelsen, 2006. "An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants," Biometrika, Biometrika Trust, vol. 93(2), pages 451-458, June.
- H�vard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392.
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