Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Computation and visualization of posterior densities in scalar nonlinear and non-Gaussian Bayesian filtering and smoothing problems
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2017 (English)In: 2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2017, p. 4686-4690Conference paper, Published paper (Refereed)
Abstract [en]

One-dimensional Bayesian filtering and smoothing problems can be solved numerically using a number of algorithms, even in nonlinear and non-Gaussian cases. In this educational paper we advocate for the benefits of visualizing the obtained posterior densities as complement to, e.g., estimation error analysis. In addition to a review of Bayesian filtering and smoothing and the respective point mass and particle solutions, we devise a novel algorithm for filtering when the likelihood cannot be evaluated. Several instructive examples are discussed and easily adjustable matlab code is provided as complement to this paper.

Place, publisher, year, edition, pages
IEEE , 2017. p. 4686-4690
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keyword [en]
Bayesian filtering; smoothing; point mass filter; particle filter
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-144282DOI: 10.1109/ICASSP.2017.7953045ISI: 000414286204169ISBN: 978-1-5090-4117-6 OAI: oai:DiVA.org:liu-144282DiVA, id: diva2:1173608
Conference
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Note

Funding Agencies|project Scalable KaIman Filters - Swedish Research Council (VR)

Available from: 2018-01-12 Created: 2018-01-12 Last updated: 2018-02-09

Open Access in DiVA

fulltext(276 kB)9 downloads
File information
File name FULLTEXT02.pdfFile size 276 kBChecksum SHA-512
823a6d343e70c431c4f03f0f7705c269d698b197481aa0a35067c491d675352598bbbd6b4d08fbead088b7ebff88ad580c532ef912b0bcf263dc9b936deb71e2
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Roth, MichaelGustafsson, Fredrik
By organisation
Automatic ControlFaculty of Science & Engineering
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 9 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 48 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf