On Persistence of Convergence of Kernel Density Estimates in Particle Filtering

0505260 - ÚI 2021 RIV CH eng C - Conference Paper (international conference)
Coufal, David
On Persistence of Convergence of Kernel Density Estimates in Particle Filtering.
Information Technology, Systems Research, and Computational Physics. Cham: Springer, 2020 - (Kulczycki, P.; Kacprzyk, J.; Kóczy, L.; Mesiar, R.; Wisniewski, R.), s. 339-346. Advances in Intelligent Systems and Computing, 945. ISBN 978-3-030-18057-7. ISSN 2194-5357.
[ITSRCP 2018: Conference on Information Technology, Systems Research and Computational Physics /3./. Cracow (PL), 02.07.2018-05.07.2018]
Grant - others:OP VVV - Fermilab-CZ(XE) CZ.02.1.01/0.0/0.0/16_013/0001787
Institutional support: RVO:67985807
Keywords : Particle filtering * Kernel density estimates * Convergence
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

A sufficient condition is provided for keeping the character of the filtering density in the filtering task. This is done for the Sobolev class of filtering densities. As a consequence, estimating the filtering density in particle filtering persists its convergence at any time of filtering. Specifying the condition complements previous results on using the kernel density estimates in particle filtering.
Permanent Link: http://hdl.handle.net/11104/0296741