On the use of sequential Monte Carlo methods for approximating smoothing functionals, with application to fixed parameter estimation
ESAIM. Proceedings, Tome 19 (2007), pp. 6-11
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Sequential Monte Carlo (SMC) methods have demonstrated a strong potential for inference on the state variables in Bayesian dynamic models. In this context, it is also often needed to calibrate model parameters. To do so, we consider block maximum likelihood estimation based either on EM (Expectation-Maximization) or gradient methods. In this approach, the key ingredient is the computation of smoothed sum functionals of the hidden states, for a given value of the model parameters. It has been observed by several authors that using standard SMC methods for this smoothing task requires a substantial number of particles and may be unreliable for larger observation sample sizes. We introduce a simple variant of the basic sequential smoothing approach based on forgetting ideas. This modification, which is transparent in terms of computation time, reduces the variability of the approximation of the sum functional. Under suitable regularity assumptions, it is shown that this modification indeed allows a tighter control of the Lp error of the approximation.
Affiliations des auteurs :
Jimmy Olsson 1 ; Olivier Cappé 2 ; Randal Douc 3 ; Éric Moulines 2
@article{EP_2007_19_a2,
author = {Jimmy Olsson and Olivier Capp\'e and Randal Douc and \'Eric Moulines},
title = {On the use of sequential {Monte} {Carlo} methods for approximating smoothing functionals, with application to fixed parameter estimation},
journal = {ESAIM. Proceedings},
pages = {6--11},
year = {2007},
volume = {19},
doi = {10.1051/proc:071902},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.1051/proc:071902/}
}
TY - JOUR AU - Jimmy Olsson AU - Olivier Cappé AU - Randal Douc AU - Éric Moulines TI - On the use of sequential Monte Carlo methods for approximating smoothing functionals, with application to fixed parameter estimation JO - ESAIM. Proceedings PY - 2007 SP - 6 EP - 11 VL - 19 UR - http://geodesic.mathdoc.fr/articles/10.1051/proc:071902/ DO - 10.1051/proc:071902 LA - en ID - EP_2007_19_a2 ER -
%0 Journal Article %A Jimmy Olsson %A Olivier Cappé %A Randal Douc %A Éric Moulines %T On the use of sequential Monte Carlo methods for approximating smoothing functionals, with application to fixed parameter estimation %J ESAIM. Proceedings %D 2007 %P 6-11 %V 19 %U http://geodesic.mathdoc.fr/articles/10.1051/proc:071902/ %R 10.1051/proc:071902 %G en %F EP_2007_19_a2
Jimmy Olsson; Olivier Cappé; Randal Douc; Éric Moulines. On the use of sequential Monte Carlo methods for approximating smoothing functionals, with application to fixed parameter estimation. ESAIM. Proceedings, Tome 19 (2007), pp. 6-11. doi: 10.1051/proc:071902
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