Home > Research > Publications & Outputs > Augmentation schemes for particle MCMC

Electronic data

  • PMCMCrepar4

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-015-9603-4

    Accepted author manuscript, 481 KB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Augmentation schemes for particle MCMC

Research output: Contribution to journalJournal articlepeer-review

Published
Close
<mark>Journal publication date</mark>11/2016
<mark>Journal</mark>Statistics and Computing
Issue number6
Volume26
Number of pages14
Pages (from-to)1293-1306
Publication StatusPublished
Early online date15/10/15
<mark>Original language</mark>English

Abstract

Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how particle MCMC can be generalised beyond this. Our key idea is to introduce new latent variables. We then use the MCMC moves to update the latent variables, and the particle filter to propose new values for the parameters and stochastic process given the latent variables. A generic way of defining these latent variables is to model them as pseudo-observations of the parameters or of the stochastic process. By choosing the amount of information these latent variables have about the parameters and the stochastic process we can often improve the mixing of the particle MCMC algorithm by trading off the Monte Carlo error of the particle filter and the mixing of the MCMC moves. We show that using pseudo-observations within particle MCMC can improve its efficiency in certain scenarios: dealing with initialisation problems of the particle filter; speeding up the mixing of particle Gibbs when there is strong dependence between the parameters and the stochastic process; and enabling further MCMC steps to be used within the particle filter.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/s11222-015-9603-4 Preprint version available as arXiv:1408.698