A Recursive Formulation of Possibilistic Filters

Dominik Hose; Michael Hanss

A Recursive Formulation of Possibilistic Filters

Dominik Hose, Michael Hanss

Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:180-190, 2021.

Abstract

We derive a recursive formulation of possibilistic filters that allow inference on the states in non-linear time-discrete dynamical systems in the presence of both aleatory and epistemic uncertainty with an imprecise probabilistic interpretation, and we present a particle-based implementation thereof.

Cite this Paper

BibTeX


@InProceedings{pmlr-v147-hose21a,
  title = 	 {A Recursive Formulation of Possibilistic Filters},
  author =       {Hose, Dominik and Hanss, Michael},
  booktitle = 	 {Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications},
  pages = 	 {180--190},
  year = 	 {2021},
  editor = 	 {Cano, Andrés and De Bock, Jasper and Miranda, Enrique and Moral, Serafı́n},
  volume = 	 {147},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {06--09 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v147/hose21a/hose21a.pdf},
  url = 	 {https://proceedings.mlr.press/v147/hose21a.html},
  abstract = 	 {We derive a recursive formulation of possibilistic filters that allow inference on the states in non-linear time-discrete dynamical systems in the presence of both aleatory and epistemic uncertainty with an imprecise probabilistic interpretation, and we present a particle-based implementation thereof.}
}

Endnote

%0 Conference Paper
%T A Recursive Formulation of Possibilistic Filters
%A Dominik Hose
%A Michael Hanss
%B Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications
%C Proceedings of Machine Learning Research
%D 2021
%E Andrés Cano
%E Jasper De Bock
%E Enrique Miranda
%E Serafı́n Moral	
%F pmlr-v147-hose21a
%I PMLR
%P 180--190
%U https://proceedings.mlr.press/v147/hose21a.html
%V 147
%X We derive a recursive formulation of possibilistic filters that allow inference on the states in non-linear time-discrete dynamical systems in the presence of both aleatory and epistemic uncertainty with an imprecise probabilistic interpretation, and we present a particle-based implementation thereof.

APA


Hose, D. & Hanss, M.. (2021). A Recursive Formulation of Possibilistic Filters. Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, in Proceedings of Machine Learning Research 147:180-190 Available from https://proceedings.mlr.press/v147/hose21a.html.

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