On Persistence of Convergence of Kernel Density Estimates in Particle Filtering

  • David CoufalEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)


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.


Particle filtering Kernel density estimates Convergence 



This work was supported by programme CZ.02.1.01/0.0/0.0/16_013/0001787 (OP VVV) of the Ministry of Education, Youth and Sport of the Czech Republic, institutional support RVO:67985807 and grant SVV No. 260454 of Charles University.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Czech Academy of Sciences, Institute of Computer SciencePraha 8Czech Republic
  2. 2.Faculty of Mathematics and Physics, Department of Probability and Mathematical StatisticsCharles UniversityPraha 8Czech Republic

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