Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities

Filip Tronarp; Simo Sarkka

doi:10.1109/ICASSP.2019.8682279

Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

6 Citations (Scopus)

185 Downloads (Pure)

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.

Original language	English
Title of host publication	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
Publisher	IEEE
Pages	5032-5036
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682279
Publication status	Published - 1 May 2019
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 Conference number: 44

Publication series

Name	Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Volume	2019-May
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP
Country/Territory	United Kingdom
City	Brighton
Period	12/05/2019 → 17/05/2019

Funding

Funding from Aalto ELEC Doctoral School and Academy of Finland (project 313708) is gratefully acknowledged.

Keywords

Bayesian state estimation
continuous-discrete filtering
non-linear filtering
Projection filtering

Access to Document

10.1109/ICASSP.2019.8682279

ELEC_Tronarp_Updates_in_Bayesian_ICASSPAccepted author manuscript, 242 KB

Cite this

Tronarp, F., & Sarkka, S. (2019). Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. In 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings (pp. 5032-5036). Article 8682279 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing; Vol. 2019-May). IEEE. https://doi.org/10.1109/ICASSP.2019.8682279

Tronarp, Filip ; Sarkka, Simo. / Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. pp. 5032-5036 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing).

@inproceedings{15dcd85477a049a5a692cbeba9106c6e,

title = "Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities",

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.",

keywords = "Bayesian state estimation, continuous-discrete filtering, non-linear filtering, Projection filtering",

author = "Filip Tronarp and Simo Sarkka",

year = "2019",

month = may,

day = "1",

doi = "10.1109/ICASSP.2019.8682279",

language = "English",

series = "Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing",

publisher = "IEEE",

pages = "5032--5036",

booktitle = "44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings",

address = "United States",

note = "IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP ; Conference date: 12-05-2019 Through 17-05-2019",

}

Tronarp, F & Sarkka, S 2019, Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. in 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings., 8682279, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2019-May, IEEE, pp. 5032-5036, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12/05/2019. https://doi.org/10.1109/ICASSP.2019.8682279

Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. / Tronarp, Filip; Sarkka, Simo.
44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE, 2019. p. 5032-5036 8682279 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities

AU - Tronarp, Filip

AU - Sarkka, Simo

N1 - Conference code: 44

PY - 2019/5/1

Y1 - 2019/5/1

N2 - 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.

AB - 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.

KW - Bayesian state estimation

KW - continuous-discrete filtering

KW - non-linear filtering

KW - Projection filtering

UR - https://www.scopus.com/pages/publications/85068971999

U2 - 10.1109/ICASSP.2019.8682279

DO - 10.1109/ICASSP.2019.8682279

M3 - Conference article in proceedings

AN - SCOPUS:85068971999

T3 - Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing

SP - 5032

EP - 5036

BT - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings

PB - IEEE

T2 - IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 12 May 2019 through 17 May 2019

ER -

Tronarp F, Sarkka S. Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. In 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE. 2019. p. 5032-5036. 8682279. (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing). doi: 10.1109/ICASSP.2019.8682279

Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities

Abstract

Publication series

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this