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Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/635
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CREEM_technical_report_2007_6_Incorporating Model Uncertainty into the Sequential Importance Sampling Framework.pdf930.34 kBAdobe PDFView/Open
Title: Incorporating Model Uncertainty into the Sequential Importance Sampling Framework using a Model Averaging Approach, or Trans-Dimensional Sequential Importance Sampling.
Authors: Lynam, Christopher
King, Ruth, 1977-
Thomas, Len
Buckland, Stephen T.
Keywords: particle filtering
model space
sequential Monte Carlo
Markov chain
Issue Date: 2007
Citation: CREEM technical report ; 2007-06
Abstract: A sequential Bayesian Monte Carlo approach is proposed in which model space can be explored during the Sequential Importance Sampling (SIS, a.k.a. Particle Filtering) fitting process. The algorithm allows model space to be explored while filtering forwards through time and takes a similar approach to Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategies, whereby parameters jump into and out of the model structure. Possible efficiency gains of the new Trans-Dimensional SIS routine are discussed and the approach is considered most beneficial when the exploration of large model space in the SIS framework is desired.
Version: Postprint
Description: Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000463/
URI: http://hdl.handle.net/10023/635
Type: Report
Publication Status: Not published
Status: Non peer reviewed
Publisher: CREEM, University of St Andrews
Appears in Collections:Centre for Research into Ecological & Environmental Modelling (CREEM) Technical report series



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