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Publication detail
PAPEŽ, M.
Original Title
Approximate Bayesian inference methods for mixture filtering with known model of switching
Type
conference paper
Language
English
Original Abstract
Bayesian inference has proven itself to be a practically useful tool for many scientific fields. The exact Bayesian inference is, however, possible in only a narrow class of probabilistic models enjoying the conjugacy principle. It is rather typical that the principle does not hold, and therefore the approximate Bayesian inference methods are taken into account. The present paper compares some of these techniques on a mixture filtering problem. The methods are presented in a generic way, considering that the mixture components are members of the exponential family of probability distributions and that the Markov model of switching between the mixture components is known. A particular instance of the methods is given for a mixture of normal linear state space models, and experiments evaluating the estimation precision and computational time are performed.
Keywords
Approximate Bayesian inference methods, decision-making theory, probabilistic mixtures, computational statistics
Authors
Released
30. 6. 2016
Publisher
Institute of Electrical and Electronics Engineers
Location
Tatranska Lomnica
ISBN
978-1-4673-8606-7
Book
Proceedings of the 17th International Carpathian Control Conference, ICCC 2016
Pages from
545
Pages to
551
Pages count
7
URL
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7501157&isnumber=7501055
BibTex
@inproceedings{BUT127522, author="Milan {Papež}", title="Approximate Bayesian inference methods for mixture filtering with known model of switching", booktitle="Proceedings of the 17th International Carpathian Control Conference, ICCC 2016", year="2016", pages="545--551", publisher="Institute of Electrical and Electronics Engineers", address="Tatranska Lomnica", doi="10.1109/CarpathianCC.2016.7501157", isbn="978-1-4673-8606-7", url="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7501157&isnumber=7501055" }