Geodesic
Combined use of importance weights and resampling weights in sequential Monte Carlo methods
Francois Le Gland1
1 IRISA / INRIA, Campus de Beaulieu, 35042 RENNES Cédex, France
ESAIM. Proceedings, Tome 19 (2007), pp. 85-100 Cet article a éte moissonné depuis la source EDP Sciences

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A particle approximation of Feynman–Kac distributions is presented here, that combines SIS and SIR algorithms in the sense that only a part of the importance weights is used for resampling, and two different approaches are proposed to analyze its performance. The first approach is based on a representation in terms of path–space distributions, and could be used to analyze the joint particle approximation of distributions for a reference model and several alternate models at the same time. The second approach, which is of independent interest and seems very promising, is based on a representation in terms of a multiplicative functional, and could be used to analyze particle approximation with adaptive resampling schemes.
DOI : 10.1051/proc:071912

Francois Le Gland 1

1 IRISA / INRIA, Campus de Beaulieu, 35042 RENNES Cédex, France 
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     author = {Francois Le Gland},
     title = {Combined use of importance weights and resampling weights in sequential {Monte} {Carlo} methods},
     journal = {ESAIM. Proceedings},
     pages = {85--100},
     year = {2007},
     volume = {19},
     doi = {10.1051/proc:071912},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.1051/proc:071912/}
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Francois Le Gland. Combined use of importance weights and resampling weights in sequential Monte Carlo methods. ESAIM. Proceedings, Tome 19 (2007), pp. 85-100. doi: 10.1051/proc:071912

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