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Publication detail
PAPEŽ, M.
Original Title
Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems
Type
conference paper
Language
English
Original Abstract
Physical systems switching between various working regimes are often encountered in practical applications. However, transition probabilities, according to which a system switches from the current regime to another one, are commonly designed as a priori known parameters, and their misspecification can degrade the performance of the algorithms filtering (or estimating) latent variables of the system. To overcome the misspecification, the present paper proposes a novel Sequential Monte Carlo procedure for estimating the transition probabilities. More specifically, it extends the concept of Rao-Blackwellization to the Dirichlet distribution, which represents the model of these probabilities. The experiments show that the proposed technique outperforms some of the classical methods in terms of the estimation precision and also the precision stability.
Keywords
Sequential Monte Carlo methods, Rao-Blackwellized particle filter, probabilistic mixtures, switching state-space models
Authors
Released
4. 8. 2016
Publisher
International Society of Information Fusion
Location
Heidelberg
ISBN
978-1-5090-2012-6
Book
Proceedings of the 19th International Conference on Information Fusion, FUSION 2016
Pages from
1063
Pages to
1070
Pages count
8
URL
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7528003&isnumber=7527857
BibTex
@inproceedings{BUT127524, author="Milan {Papež}", title="Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems", booktitle="Proceedings of the 19th International Conference on Information Fusion, FUSION 2016", year="2016", pages="1063--1070", publisher="International Society of Information Fusion", address="Heidelberg", isbn="978-1-5090-2012-6", url="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7528003&isnumber=7527857" }