Adaptive methods for sequential importance sampling with application to state space models
(2008) In Statistics and Computing 18(4). p.461-480- Abstract
- In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms—also known as particle filters—relying on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation (suggested by Kong et al. in J. Am. Stat. Assoc. 89(278–288):590–599, 1994) of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which this chi-square distance is minimal. Moreover, we... (More)
- In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms—also known as particle filters—relying on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation (suggested by Kong et al. in J. Am. Stat. Assoc. 89(278–288):590–599, 1994) of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which this chi-square distance is minimal. Moreover, we establish an empirical estimate of linear complexity of the Kullback-Leibler divergence between the involved distributions. Guided by these results, we discuss adaptive designing of the particle filter proposal distribution and illustrate the methods on a numerical example. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1271958
- author
- Cornebise, Julien ; Moulines, Éric and Olsson, Jimmy LU
- organization
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Adaptive Monte Carlo, Auxiliary particle filter, Coefficient of variation, Kullback-Leibler divergence, Cross-entropy method, Sequential Monte Carlo, State space models
- in
- Statistics and Computing
- volume
- 18
- issue
- 4
- pages
- 461 - 480
- publisher
- Springer
- external identifiers
-
- wos:000261607600009
- scopus:57849133035
- ISSN
- 0960-3174
- DOI
- 10.1007/s11222-008-9089-4
- language
- English
- LU publication?
- yes
- id
- 71dba19a-b52d-42d4-91d7-32b3692aab1c (old id 1271958)
- date added to LUP
- 2016-04-01 13:25:53
- date last changed
- 2022-04-06 05:04:36
@article{71dba19a-b52d-42d4-91d7-32b3692aab1c,
abstract = {{In this paper we discuss new adaptive proposal strategies for sequential Monte Carlo algorithms—also known as particle filters—relying on criteria evaluating the quality of the proposed particles. The choice of the proposal distribution is a major concern and can dramatically influence the quality of the estimates. Thus, we show how the long-used coefficient of variation (suggested by Kong et al. in J. Am. Stat. Assoc. 89(278–288):590–599, 1994) of the weights can be used for estimating the chi-square distance between the target and instrumental distributions of the auxiliary particle filter. As a by-product of this analysis we obtain an auxiliary adjustment multiplier weight type for which this chi-square distance is minimal. Moreover, we establish an empirical estimate of linear complexity of the Kullback-Leibler divergence between the involved distributions. Guided by these results, we discuss adaptive designing of the particle filter proposal distribution and illustrate the methods on a numerical example.}},
author = {{Cornebise, Julien and Moulines, Éric and Olsson, Jimmy}},
issn = {{0960-3174}},
keywords = {{Adaptive Monte Carlo; Auxiliary particle filter; Coefficient of variation; Kullback-Leibler divergence; Cross-entropy method; Sequential Monte Carlo; State space models}},
language = {{eng}},
number = {{4}},
pages = {{461--480}},
publisher = {{Springer}},
series = {{Statistics and Computing}},
title = {{Adaptive methods for sequential importance sampling with application to state space models}},
url = {{http://dx.doi.org/10.1007/s11222-008-9089-4}},
doi = {{10.1007/s11222-008-9089-4}},
volume = {{18}},
year = {{2008}},
}