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Bayesian source detection and parameter estimation of a plume model based on sensor network measurements
Author(s) -
Huang Chunfeng,
Hsing Tailen,
Cressie Noel,
Ganguly Auroop R.,
Protopopescu Vladimir A.,
Rao Nageswara S.
Publication year - 2010
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.859
Subject(s) - markov chain monte carlo , plume , bayesian inference , computer science , monte carlo method , algorithm , bayesian probability , inference , diffusion , markov chain , parameter space , estimation theory , mathematics , statistics , artificial intelligence , physics , machine learning , thermodynamics
We consider a network of sensors that measure the intensities of a complex plume composed of multiple absorption–diffusion source components. We address the problem of estimating the plume parameters, including the spatial and temporal source origins and the parameters of the diffusion model for each source, based on a sequence of sensor measurements. The approach not only leads to multiple‐source detection, but also the characterization and prediction of the combined plume in space and time. The parameter estimation is formulated as a Bayesian inference problem, and the solution is obtained using a Markov chain Monte Carlo algorithm. The approach is applied to a simulation study, which shows that an accurate parameter estimation is achievable. Copyright © 2010 John Wiley & Sons, Ltd.