Adaptive sequential Monte Carlo by means of mixture of experts
(2014) In Statistics and Computing 24(3). p.317-337- Abstract
- Appropriately designing the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm adaptively approximating the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via online-EM methods, to the optimal kernel through minimisation of the Kullback-Leibler divergence between the auxiliary target and instrumental... (More)
- Appropriately designing the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm adaptively approximating the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via online-EM methods, to the optimal kernel through minimisation of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimisation problem for the whole particle sample, yielding an algorithm with only linear complexity. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4438916
- author
- Cornebise, Julien ; Moulines, Eric and Olsson, Jimmy LU
- organization
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Optimal proposal kernel, Adaptive algorithms, Kullback-Leibler, divergence, Coefficient of variation, Expectation-maximisation, Particle, filter, Sequential Monte Carlo, Shannon entropy
- in
- Statistics and Computing
- volume
- 24
- issue
- 3
- pages
- 317 - 337
- publisher
- Springer
- external identifiers
-
- wos:000334435400003
- scopus:84898548772
- ISSN
- 0960-3174
- DOI
- 10.1007/s11222-012-9372-2
- language
- English
- LU publication?
- yes
- id
- d93994fd-09a4-461c-bf79-430d075ba6fb (old id 4438916)
- date added to LUP
- 2016-04-01 12:57:49
- date last changed
- 2022-02-26 18:33:19
@article{d93994fd-09a4-461c-bf79-430d075ba6fb,
abstract = {{Appropriately designing the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm adaptively approximating the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via online-EM methods, to the optimal kernel through minimisation of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimisation problem for the whole particle sample, yielding an algorithm with only linear complexity. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models.}},
author = {{Cornebise, Julien and Moulines, Eric and Olsson, Jimmy}},
issn = {{0960-3174}},
keywords = {{Optimal proposal kernel; Adaptive algorithms; Kullback-Leibler; divergence; Coefficient of variation; Expectation-maximisation; Particle; filter; Sequential Monte Carlo; Shannon entropy}},
language = {{eng}},
number = {{3}},
pages = {{317--337}},
publisher = {{Springer}},
series = {{Statistics and Computing}},
title = {{Adaptive sequential Monte Carlo by means of mixture of experts}},
url = {{http://dx.doi.org/10.1007/s11222-012-9372-2}},
doi = {{10.1007/s11222-012-9372-2}},
volume = {{24}},
year = {{2014}},
}