A sequential particle filter method for static models

Biometrika

Volumn 89, Issue 3, 2002, Pages 539-551

  Chopin, Nicolas ^a  

^a Ecole Nationale de la Statistique et de l'Administration Economique   (France)

Author keywords

Batch importance sampling; Generalised linear model; Importance sampling; Markov chain Monte Carlo; Metropolis Hastings; Mixture model; Parallel processing; Particle filter

Indexed keywords

EID: 0012338718     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/89.3.539     Document Type: Review

References (11)

1. ALBERT, J. H. & CHIB, S. (1993). Bayesian analysis of binary and polychotomous response data. J. Am. Statist. Assoc. 88, 669-79.
2. CARPENTER, J., CLIFFORD, P. & FEARNHEAD, P. (1999). An improved particle filter for nonlinear problems. IER Proc. Radar, Sonar Navig. 146, 2-7.
3. CELEUX, G., HURN, M. & ROBERT, C. P. (2000). Computational and inferential difficulties with mixture posterior distributions. J. Am. Statist. Assoc. 95, 957-70.
4. DELLAPORTAS, P. & SMITH, A. F. M. (1993). Bayesian inference for generalised linear and proportional hazards models via Gibbs sampling. Appl. Statist. 42, 443-59.
5. DOUCET, A., DE FREITAS, N. & GORDON, N. J. (2001). Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag.
6. GEWEKE, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica 57, 1317-39.
7. GILKS, W. R. & BERZUINI, C. (2001). Following a moving target: Monte Carlo inference for dynamic Bayesian models. J. R. Statist. Soc. B 63, 127-46.
8. GORDON, N. J., SALMOND, D. J. & SMITH, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. Radar Sig. Proces. 140, 107-13.
9. LIU, J. & CHEN, R. (1998). Sequential Monte Carlo methods for dynamic systems. J. Am. Statist. Assoc. 93, 1032-44.
10. MCCULLAGH, P. & NELDER, J. A. (1989). Generalized Linear Models, 2nd ed. London: Chapman and Hall.
11. ROBERT, C.P. & MENGERSEN, K. L. (1999). Reparameterisation issues in mixture modelling and their bearing on MCMC algorithms. Comp. Statist. Data Anal. 29, 325-43.