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PAPEŽ, M.
Originální název
Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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.
Klíčová slova
Sequential Monte Carlo methods, Rao-Blackwellized particle filter, probabilistic mixtures, switching state-space models
Autoři
Vydáno
4. 8. 2016
Nakladatel
International Society of Information Fusion
Místo
Heidelberg
ISBN
978-1-5090-2012-6
Kniha
Proceedings of the 19th International Conference on Information Fusion, FUSION 2016
Strany od
1063
Strany do
1070
Strany počet
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" }