Publication detail

A projection-based Rao-Blackwellized particle filter to estimate parameters in conditionally conjugate state-space models

PAPEŽ, M.

Original Title

A projection-based Rao-Blackwellized particle filter to estimate parameters in conditionally conjugate state-space models

Type

conference paper

Language

English

Original Abstract

Particle filters constitute today a well-established class of techniques for state filtering in non-linear state-space models. However, online estimation of static parameters under the same framework represents a difficult problem. The solution can be found to some extent within a category of state-space models allowing us to perform parameter estimation in an analytically tractable manner, while still considering non-linearities in data evolution equations. Nevertheless, the well-known particle path degeneracy problem complicates the computation of the statistics that are required to estimate the parameters. The present paper proposes a simple and efficient method which is experimentally shown to suffer less from this issue.

Keywords

Sequential Monte Carlo; particle filtering; conditionally conjugate state-space models; Rao-Blackwellization

Authors

PAPEŽ, M.

Released

10. 6. 2018

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Location

New York

ISBN

978-1-5386-1571-3

Book

Proceedings of the 20th Statistical Signal Processing Workshop (SSP)

Pages from

268

Pages to

272

Pages count

5

URL

BibTex

@inproceedings{BUT148842,
  author="Milan {Papež}",
  title="A projection-based Rao-Blackwellized particle filter to estimate parameters in conditionally conjugate state-space models",
  booktitle="Proceedings of the 20th Statistical Signal Processing Workshop (SSP)",
  year="2018",
  pages="268--272",
  publisher="Institute of Electrical and Electronics Engineers (IEEE)",
  address="New York",
  doi="10.1109/SSP.2018.8450730",
  isbn="978-1-5386-1571-3",
  url="https://www.semanticscholar.org/paper/A-Projection-Based-Rao-Blackwellized-Particle-to-in-Papez/88052a94e61c6fb9b0b27e178de9a59e707ea737"
}