Bayesian Filtering for States Uniformly Distributed on a Parallelotopic Support

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00519515" target="_blank" >RIV/67985556:_____/19:00519515 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ISSPIT47144.2019.9001829" target="_blank" >http://dx.doi.org/10.1109/ISSPIT47144.2019.9001829</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ISSPIT47144.2019.9001829" target="_blank" >10.1109/ISSPIT47144.2019.9001829</a>

Result language
angličtina
Original language name
Bayesian Filtering for States Uniformly Distributed on a Parallelotopic Support
Original language description
This paper contributes to the literature on Bayesian filtering in the case where the processes driving the states and observations are uniformly distributed on finite intervals. We introduce the class of uniform distributions on parallelotopic supports (UPS). We derive optimal local distributional projections (i.e. approximations) within this UPS class-in the sense of minimum Kullback-Leibler divergence-of the outputs of the data and time updates of filtering. We demonstrate that the UPS class provides a tighter approximation (and therefore more precise inferences) than a previously reported approximation on orthotopic supports. It does this, while still achieving bounded complexity in the resulting recursive filtering algorithm. The comparative performance of the UPS-closed filtering algorithm is explored-via both Bayesian and frequentist performance measures-as a function of signal-to-noise ratio and state dimension in a position-velocity system.
Czech name
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Czech description
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Project
<a href="/en/project/GA18-15970S" target="_blank" >GA18-15970S: Optimal Distributional Design for External Stochastic Knowledge Processing</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the IEEE International Symposium on Signal Processing and Information Technology 2019 (ISSPIT 2019)
ISBN
978-1-7281-5341-4
ISSN
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e-ISSN
—
Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Piscataway
Event location
Ajman
Event date
Dec 10, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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