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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

4 Sitaatiot (Scopus)

180 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings
Kustantaja	IEEE
Sivut	5032-5036
Sivumäärä	5
ISBN (elektroninen)	9781479981311
DOI - pysyväislinkit	https://doi.org/10.1109/ICASSP.2019.8682279
Tila	Julkaistu - 1 toukok. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	IEEE International Conference on Acoustics, Speech, and Signal Processing - Brighton, Iso-Britannia Kesto: 12 toukok. 2019 → 17 toukok. 2019 Konferenssinumero: 44

Julkaisusarja

Nimi	Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Vuosikerta	2019-May
ISSN (painettu)	1520-6149
ISSN (elektroninen)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing
Lyhennettä	ICASSP
Maa/Alue	Iso-Britannia
Kaupunki	Brighton
Ajanjakso	12/05/2019 → 17/05/2019

Pääsy asiakirjaan

10.1109/ICASSP.2019.8682279

ELEC_Tronarp_Updates_in_Bayesian_ICASSPAccepted author manuscript, 242 KB

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Link to publication in Scopus

Siteeraa tätä

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

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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",

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Tronarp, F & Sarkka, S 2019, Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. julkaisussa 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, Vuosikerta 2019-May, IEEE, Sivut 5032-5036, IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, Iso-Britannia, 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. s. 5032-5036 8682279 (Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing; Vuosikerta 2019-May).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

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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

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M3 - Conference article in proceedings

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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

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Tronarp F, Sarkka S. Updates in Bayesian Filtering by Continuous Projections on a Manifold of Densities. julkaisussa 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019; Brighton; United Kingdom; 12-17 May 2019 : Proceedings. IEEE. 2019. s. 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

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