Sequential Monte-Carlo Based Framework for Dynamic Data-Driven Event Reconstruction for Atmospheric Release Article Swipe

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G. Jóhannesson , Fotini Katopodes Chow , L Glascoe , Ronald E. Glaser , W Hanley , Branko Kosović , Milovan Krnjajić , Shawn Larsen , Julie K. Lundquist , A.A. Mirin , J.J. Nitao , G Sugiyama ·

YOU? · · 2021 · Open Access ·

Atmospheric releases of hazardous materials are highly effective means to impact large populations. We propose an atmospheric event reconstruction framework that couples observed data and predictive computer-intensive dispersion models via Bayesian methodology. Due to the complexity of the model framework, a sampling-based approach is taken for posterior inference that combines Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) strategies.

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Monte Carlo method Computer science Event (particle physics) Physics Mathematics Astrophysics Statistics

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Title: Sequential Monte-Carlo Based Framework for Dynamic Data-Driven Event Reconstruction for Atmospheric Release

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Language: en

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Publication year: 2021

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Publication date: 2021-05-04

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Authors: G. Jóhannesson, Fotini Katopodes Chow, L Glascoe, Ronald E. Glaser, W Hanley, Branko Kosović, Milovan Krnjajić, Shawn Larsen, Julie K. Lundquist, A.A. Mirin, J.J. Nitao, G Sugiyama

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Concepts: Monte Carlo method, Computer science, Event (particle physics), Physics, Mathematics, Astrophysics, Statistics

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Cited by: 8

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