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Time Series Analysis by State Space MethodsSecond Edition$
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James Durbin and Siem Jan Koopman

Print publication date: 2012

Print ISBN-13: 9780199641178

Published to Oxford Scholarship Online: December 2013

DOI: 10.1093/acprof:oso/9780199641178.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 22 January 2022

Particle filtering

Particle filtering

Chapter:
(p.276) 12 Particle filtering
Source:
Time Series Analysis by State Space Methods
Author(s):

J. Durbin

S.J. Koopman

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199641178.003.0012

This chapter discusses the filtering of non-Gaussian and nonlinear series by fixing the sample at the values previously obtained at times …, t − 2, t − 1 and choosing a fresh value at time t only. A new recursion over time is then required for the resulting simulation. The method is called particle filtering. The chapter is organized as follows. Section 12.2 considers filtering by the method of Chapter 11. Section 12.3 discusses resampling techniques designed to reduce degeneracy in sampling. Section 12.4 describes six methods of particle filtering, namely bootstrap filtering, auxiliary particle filtering, the extended particle filter, the unscented particle filter, the local regression filter, and the mode equalisation filter.

Keywords:   non-Gaussian models, nonlinear models, bootstrap filtering, auxiliary particle filtering, extended particle filter, unscented particle filter, local regression filter, mode equalisation filter

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