NDLI: Non-linear Bayesian CBRN source term estimation

Content Provider	IEEE Xplore Digital Library
Author	Robins, P. Thomas, P.
Copyright Year	2005
Description	Author affiliation: Dstl, Hazard Assessment, Simulation & Prediction Group, Porton Down, UK (Robins, P.; Thomas, P.)
Abstract	A Bayesian posterior probability density sampling algorithm suitable for use in estimating chemical, biological, radiological or nuclear atmospheric releases of material is proposed. The estimation problem is different from many other fusion problems, in that there are no state evolution equations, the forward model is highly non-linear and the likelihoods are non-Gaussian. The algorithm is able to use stored output from complex atmospheric dispersion models for more rapid update of the posterior from new data without having to re-run the models. The use of differential evolution Monte Carlo allows new samples to rapidly diverge from degenerate sample sets. Results for inferences made in a slightly simplified environment of chemical releases only are presented, demonstrating that the sampling scheme performs adequately despite constraints of a short time span to calculate inferences from complex likelihood calculations. Extensions to wider applications and improvements to the algorithm are discussed.
File Size	4419817
File Format	PDF
ISBN	0780392868
DOI	10.1109/ICIF.2005.1591980
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Atmospheric modeling Sampling methods Chemicals Evolution (biology) Inference algorithms Biological materials State estimation Differential equations Nonlinear equations non-Gaussian Bayesian MCMC DE-MC non-linear estimation
Content Type	Text
Resource Type	Article

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