Publication detail

Rao-Blackwellized particle Gibbs kernels for smoothing in jump Markov nonlinear models

PAPEŽ, M.

Original Title

Rao-Blackwellized particle Gibbs kernels for smoothing in jump Markov nonlinear models

Type

conference paper

Language

English

Original Abstract

Jump Markov nonlinear models (JMNMs) characterize a dynamical system by a finite number of presumably nonlinear and possibly non-Gaussian state-space configurations that switch according to a discrete-valued hidden Markov process. In this context, the smoothing problem - the task of estimating fixed points or sequences of hidden variables given all available data -is of key relevance to many objectives of statistical inference, including the estimation of static parameters. The present paper proposes a particle Gibbs with ancestor sampling (PGAS)-based smoother for JMNMs. The design methodology relies on integrating out the discrete process in order to increase the efficiency through Rao-Blackwellization. The experimental evaluation illustrates that the proposed method achieves higher estimation accuracy in less computational time compared to the original PGAS procedure.

Keywords

Sequential Monte Carlo; particle filtering; particle smoothing; particle Markov chain Monte Carlo; particle Gibbs with ancestor sampling; jump Markov nonlinear models; Rao-Blackwellization

Authors

PAPEŽ, M.

Released

12. 6. 2018

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

ISBN

9783952426999

Book

Proceedings of the 16th European Control Conference, ECC 2018

Pages from

2466

Pages to

2471

Pages count

6

BibTex

@inproceedings{BUT148840,
  author="Milan {Papež}",
  title="Rao-Blackwellized particle Gibbs kernels for smoothing in jump Markov nonlinear models",
  booktitle="Proceedings of the 16th European Control Conference, ECC 2018",
  year="2018",
  pages="2466--2471",
  publisher="Institute of Electrical and Electronics Engineers (IEEE)",
  doi="10.23919/ECC.2018.8550408",
  isbn="9783952426999"
}