Bayesian Inference and Markov Chain Monte Carlo Sampling to Reconstruct a Contaminant Source on a Continental Scale | Zendy

Luca Delle Monache | Zendy; Julie K. Lundquist | Zendy; Branko Kosović | Zendy; Gardar Johannesson | Zendy; K Dyer | Zendy; Roger D. Aines | Zendy; Fotini Katopodes Chow | Zendy; R Belles | Zendy; William G. Hanley | Zendy; Shawn Larsen | Zendy; G Loosmore | Zendy; J.J. Nitao | Zendy; G Sugiyama | Zendy; Philip J. Vogt | Zendy

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Bayesian Inference and Markov Chain Monte Carlo Sampling to Reconstruct a Contaminant Source on a Continental Scale

Author(s) -

Luca Delle Monache,

Julie K. Lundquist,

Branko Kosović,

Gardar Johannesson,

K Dyer,

Roger D. Aines,

Fotini Katopodes Chow,

R Belles,

William G. Hanley,

Shawn Larsen,

G Loosmore,

J.J. Nitao,

G Sugiyama,

Philip J. Vogt

Publication year - 2008

Publication title -

journal of applied meteorology and climatology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.079

H-Index - 134

eISSN - 1558-8432

pISSN - 1558-8424

DOI - 10.1175/2008jamc1766.1

Subject(s) - markov chain monte carlo , bayesian probability , computer science , monte carlo method , bayesian inference , sampling (signal processing) , markov chain , algorithm , statistics , mathematical optimization , mathematics , artificial intelligence , machine learning , filter (signal processing) , computer vision

A methodology combining Bayesian inference with Markov chain Monte Carlo (MCMC) sampling is applied to a real accidental radioactive release that occurred on a continental scale at the end of May 1998 near Algeciras, Spain. The source parameters (i.e., source location and strength) are reconstructed from a limited set of measurements of the release. Annealing and adaptive procedures are implemented to ensure a robust and effective parameter-space exploration. The simulation setup is similar to an emergency response scenario, with the simplifying assumptions that the source geometry and release time are known. The Bayesian stochastic algorithm provides likely source locations within 100 km from the true source, after exploring a domain covering an area of approximately 1800 km × 3600 km. The source strength is reconstructed with a distribution of values of the same order of magnitude as the upper end of the range reported by the Spanish Nuclear Security Agency. By running the Bayesian MCMC algorit...

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