Adaptive importance sampling in general mixture classes

Statistics and Computing

Volumn 18, Issue 4, 2008, Pages 447-459

  Cappé, Olivier ^a,e   Douc, Randal ^b   Guillin, Arnaud ^c   Marin, Jean Michel ^d   Robert, Christian P ^e,f  

^a CNRS   (France)

^b TELECOM SUDPARIS   (France)

^c Ecole Centrale Marseille   (France)

^d INRIA SACLAY   (France)

^e CREST   (France)

^f UNIVERSITÉ PARIS DAUPHINE   (France)

Author keywords

Adaptive Monte Carlo; EM algorithm; Entropy; Importance sampling; Kullback Leibler divergence; Mixture model; Population Monte Carlo

Indexed keywords

EID: 57849141688     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-008-9059-x     Document Type: Article

References (14)

1. O. Cappé A. Guillin J. Marin C. Robert 2004 Population Monte Carlo J. Comput. Graph. Stat. 13 4 907 929
2. Cappé, O., Moulines, E., Rydén, T.: Inference in Hidden Markov Models. Springer, New York (2005)
3. R. Chen J.S. Liu 1996 Predictive updating method and Bayesian classification J. R. Stat. Soc. Ser. B 58 2 397 415
4. R. Douc A. Guillin J.-M. Marin C. Robert 2007 Convergence of adaptive mixtures of importance sampling schemes Ann. Stat. 35 1 420 448
5. R. Douc A. Guillin J.-M. Marin C. Robert 2007 Minimum variance importance sampling via population Monte Carlo ESAIM: Probab. Stat. 11 427 447
6. Doucet, A., de Freitas, N., Gordon, N.: Sequential Monte Carlo Methods in Practice. Springer, New York (2001)
7. J. Geweke 1989 Bayesian inference in econometric models using Monte Carlo integration Econometrica 57 1317 1340
8. T. Hesterberg 1995 Weighted average importance sampling and defensive mixture distributions Technometrics 37 2 185 194
9. M. Oh J. Berger 1993 Integration of multimodal functions by Monte Carlo importance sampling J. Am. Stat. Assoc. 88 450 456
10. D. Peel G. [tmp] McLachlan 2000 Robust mixture modelling using the t distribution Stat. Comput. 10 339 348
11. R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2006)
12. Robert, C., Casella, G.: Monte Carlo Statistical Methods, 2nd edn. Springer, New York (2004)
13. Rubinstein, R.Y., Kroese, D.P.: The Cross-Entropy Method. Springer, New York (2004)
14. West, M.: Modelling with mixtures. In: Berger, J., Bernardo, J., Dawid, A., Smith, A. (eds.) Bayesian Statistics, vol. 4, pp. 503-525. Oxford University Press, Oxford (1992)