A sequential particle filter method for static models

Nicolas Chopin

Biometrika, 2002, vol. 89, issue 3, 539-552

Abstract: Particle filter methods are complex inference procedures, which combine importance sampling and Monte Carlo schemes in order to explore consistently a sequence of multiple distributions of interest. We show that such methods can also offer an efficient estimation tool in 'static' set-ups, in which case &pgr;(&thgr; | y-sub-1, …, y-sub-N) (n

Date: 2002
References: Add references at CitEc
Citations: View citations in EconPapers (108)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: A Sequential Particle Filter Method for Static Models (2000)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:89:y:2002:i:3:p:539-552

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().