A Recursive Formulation of Possibilistic Filters

Dominik Hose, Michael Hanss
Proceedings of the Twelveth International Symposium on Imprecise Probability: Theories and Applications, PMLR 147:180–190-180–190, 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--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--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 147:180–190-180–190 Available from https://proceedings.mlr.press/v147/hose21a.html.

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