MCMC methods for putative pollution source problems in environmental epidemiology.
This paper demonstrates the use of the Gibbs Sampler and other Markov Chain Monte Carlo (MCMC) methods in two applications in environmental epidemiology. The first example concerns the application of a Metropolis-Hastings/Gibbs sampler to a Cox process with a direction-dependent cluster variance parameter. The second example consists of the estimation of the posterior (spatial) distribution of a putative location.
Lawson
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Department of Mathematical & Computer Sciences, University of Abertay Dundee, U.K.
D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004787:Environmental Pollution / Q000706:statistics & numerical data*; D006801:Humans; D007220:Industrial Waste / Q000009:adverse effects; D007822:Laryngeal Neoplasms / Q000453:epidemiology / Q000209:etiology; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method
D007220:Industrial Waste