Theory for reconstruction of an unknown number of contaminant sources using probabilistic inference

Boundary-Layer Meteorology

Volumn 127, Issue 3, 2008, Pages 359-394

  Yee, Eugene ^a  

^a DEFENCE RESEARCH AND DEVELOPMENT CANADA   (Canada)

Author keywords

Adjoint systems; Bayesian inference; Lagrangian stochastic models; Markov chain Monte Carlo; Source reconstruction

Indexed keywords

BAYESIAN NETWORKS; INVERSE PROBLEMS; MARKOV PROCESSES; MONTE CARLO METHODS; PROBABILITY DENSITY FUNCTION; STOCHASTIC MODELS;

ADJOINT SYSTEMS; LAGRANGIAN STOCHASTIC MODELS; MULTIPLE SOURCE DISTRIBUTION; SOURCE RECONSTRUCTION;

AIR POLLUTION;

AIR POLLUTION; BAYESIAN NETWORKS; INVERSE PROBLEMS; MARKOV PROCESSES; MONTE CARLO METHODS; PROBABILITY DENSITY FUNCTION; STOCHASTIC MODELS;

ADJOINT METHOD; ALGORITHM; BAYESIAN ANALYSIS; LAGRANGIAN ANALYSIS; MARKOV CHAIN; MAXIMUM LIKELIHOOD ANALYSIS; MONTE CARLO ANALYSIS; PROBABILITY; PROBABILITY DENSITY FUNCTION; RECONSTRUCTION; STOCHASTICITY;

EID: 42649145067     PISSN: 00068314     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10546-008-9270-5     Document Type: Article

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