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Inference in High Dimensional Parameter Space

O'Hare A (2015) Inference in High Dimensional Parameter Space, Journal of Computational Biology, 22 (11), pp. 997-1004.

Model parameterinferencehas become increasingly popular in recent years in the field of computational epidemiology, especially for models with a large number of parameters. Techniques such asApproximate Bayesian Computation(ABC) ormaximum/partial likelihoodsare commonly used toinferparameters in phenomenological models that best describe some set of data. These techniques rely on efficient exploration of the underlying parameter space, which is difficult in high dimensions, especially if there are correlations between the parameters in the model that may not be knowna priori. The aim of this article is to demonstrate the use of the recently invented Adaptive Metropolis algorithm for exploring parameter space in a practical way through the use of a simple epidemiological model.

bayesian; inference; Adaptive Metropolis algorithm; Monte Carlo; epidemiology; likelihood; Markov Chain

AuthorsO'Hare Anthony
Publication date11/2015
Publication date online15/07/2015
Date accepted by journal15/05/2015
PublisherMary Ann Liebert, Inc
ISSN 1066-5277

Journal of Computational Biology: Volume 22, Issue 11

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