Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities
(2019) IEEE International Conference on Acoustics, Speech, and Signal Processing 2019- Abstract
- In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The projection filtering framework is exploited to develop accurate approximations of posterior distributions within parametric classes of probability distributions. This is done by formulating an ordinary differential equation for the posterior distribution that has the prior as initial value and hits the exact posterior after a unit of time. Particular emphasis is put on exponential families, especially the Gaussian family of densities. Experimental results demonstrate the efficacy and flexibility of the method.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/b268f16f-bc93-434b-895b-d34974ad223f
- author
- Tronarp, Filip LU and Särkkä, Simo
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) : Print on Demand(PoD) ISBN: - Print on Demand(PoD) ISBN:
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- IEEE International Conference on Acoustics, Speech, and Signal Processing 2019
- conference location
- Brighton, United Kingdom
- conference dates
- 2019-05-13 - 2019-05-17
- external identifiers
-
- scopus:85068971999
- ISBN
- 9781479981311
- 9781479981328
- DOI
- 10.1109/ICASSP.2019.8682279
- language
- English
- LU publication?
- no
- id
- b268f16f-bc93-434b-895b-d34974ad223f
- date added to LUP
- 2023-08-20 23:01:19
- date last changed
- 2024-06-06 13:42:36
@inproceedings{b268f16f-bc93-434b-895b-d34974ad223f, abstract = {{In this paper, we develop a novel method for approximate continuous-discrete Bayesian filtering. The projection filtering framework is exploited to develop accurate approximations of posterior distributions within parametric classes of probability distributions. This is done by formulating an ordinary differential equation for the posterior distribution that has the prior as initial value and hits the exact posterior after a unit of time. Particular emphasis is put on exponential families, especially the Gaussian family of densities. Experimental results demonstrate the efficacy and flexibility of the method.}}, author = {{Tronarp, Filip and Särkkä, Simo}}, booktitle = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) : Print on Demand(PoD) ISBN:}}, isbn = {{9781479981311}}, language = {{eng}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities}}, url = {{http://dx.doi.org/10.1109/ICASSP.2019.8682279}}, doi = {{10.1109/ICASSP.2019.8682279}}, year = {{2019}}, }