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  • Centre for Research into Ecological & Environmental Modelling (CREEM)
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Technical report series
  • Centre for Research into Ecological & Environmental Modelling (CREEM) Technical report series
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Incorporating Model Uncertainty into the Sequential Importance Sampling Framework using a Model Averaging Approach, or Trans-Dimensional Sequential Importance Sampling.

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CREEM_technical_report_2007_6_Incorporating Model Uncertainty into the Sequential Importance Sampling Framework.pdf (930.3Kb)
Date
2007
Author
Lynam, Christopher
King, Ruth
Thomas, Len
Buckland, Stephen T.
Keywords
particle filtering
model space
sequential Monte Carlo
Markov chain
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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.
Citation
CREEM technical report ; 2007-06
Type
Report
Description
Previously in the University eprints HAIRST pilot service at http://eprints.st-andrews.ac.uk/archive/00000463/
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  • Centre for Research into Ecological & Environmental Modelling (CREEM) Technical report series
URI
http://hdl.handle.net/10023/635

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