An adaptive sequential Monte Carlo method for approximate Bayesian computation

Statistics and Computing

Volumn 22, Issue 5, 2012, Pages 1009-1020

  Del Moral, Pierre ^a   Doucet, Arnaud ^b   Jasra, Ajay ^c  

^a INRIA   (France)

^b UNIVERSITY OF OXFORD   (United Kingdom)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Approximate Bayesian computation; Markov chain Monte Carlo; Sequential Monte Carlo

Indexed keywords

EID: 84857190557     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-011-9271-y     Document Type: Article

References (24)

1. Andrieu, C., Johanes, T.: A tutorial on adaptive Markov chain Monte Carlo methods. Stat. Comput. 18, 343-373 (2008).
2. Andrieu, C., Berthelesen, K., Doucet, A., Roberts, G. O.: The expected auxiliary variable method for Monte Carlo simulation. Working paper (2008).
3. Beaumont, M. A., Cornuet, J. M., Marin, J. M., Robert, C. P.: Adaptive approximate Bayesian computation. Biometrika 86, 983-990 (2009).
4. Beskos, A., Crisan, D., Jasra, A.: On the stability of sequential Monte Carlo methods in high dimensions. Technical Report, Imperial College London (2011).
5. Bortot, P., Coles, S., Sisson, S.: Inference for stereological extremes. J. Am. Stat. Assoc. 102, 84-92 (2009).
6. Chopin, N.: A sequential particle filter for static models. Biometrika 89, 539-552 (2002).
7. Del Moral, P., Doucet, A., Jasra, A.: Sequential Monte Carlo samplers. J. R. Stat. Soc. B 68, 411-436 (2006).
8. Del Moral, P., Doucet, A., Jasra, A.: On adaptive resampling strategies for sequential Monte Carlo methods. Bernoulli (2011, to appear).
9. Drovandi, C. C., Pettit, A. N.: Estimation of parameters for macroparasite population evolution using approximate Bayesian computation. Biometrics 67, 225-233 (2011).
10. Fearnhead, P., Prangle, D.: Semi-automatic approximate Bayesian computation. Technical Report, University of Lancaster (2010).
11. Gilks, W. R., Berzuini, C.: Following a moving target-Monte Carlo inference for dynamic Bayesian models. J. R. Stat. Soc. B 63, 127-146 (2001).
12. Gramacy, R., Samworth, R., King, R.: Importance tempering. Stat. Comput. 20, 1-7 (2010).
13. Grelaud, A.: Méthodes sans vraisemblance appliquées à l'étude de la sélection naturelle et la prédiction de structure tridimensionnelle des protéines. Ph. D. Thesis, Université Paris-Dauphine (2009).
14. Kitagawa, G.: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. J. Comput. Graph. Stat. 5, 1-25 (1996).
15. Liu, J. S.: Monte Carlo Strategies in Scientific Computing. Springer, New York (2001).
16. Majoram, P., Molitor, J., Plagnol, V., Tavaré, S.: Markov chain Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 100, 15324-15328 (2003).
17. Marin, J.-M., Pudlo, P., Robert, C. P., Ryder, R.: Approximate Bayesian computational methods. Stat. Comput. (2011, to appear).
18. Neal, R. M.: Annealed importance sampling. Stat. Comput. 11, 125-139 (2001).
19. Peters, G. W., Fan, Y., Sisson, S. A.: On sequential Monte Carlo, partial rejection control and approximate Bayesian computation. Technical report, University of New South Wales (2008).
20. Sisson, S., Fan, Y., Tanaka, M. M.: Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 104, 1760-1765 (2007).
21. Sisson, S., Fan, Y., Tanaka, M. M.: Correction: Sequential Monte Carlo without likelihoods. Proc. Natl. Acad. Sci. 106, 1760 (2009).
22. Small, P. M., Hopewell, P., Singh, P., Paz, A., Parsonnet, J., et al.: The epidemiology of tuberculosis in San Francisco: a population-based study using conventional and molecular methods. N. Engl. J. Med. 330, 1703-1709 (1994).
23. Tanaka, M. M., Francis, A. R., Luciani, F., Sisson, A.: Using approximate computation to estimate tuberculosis transmission parameters from genotype data. Genetics 173, 1511-1520 (2006).
24. Toni, T., Welch, D., Strelkowa, N., Ipsen, A., Stumpf, M. P. H.: Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. J. R. Soc. Interface 6, 187-202 (2009).