Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/22224
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Inference in High Dimensional Parameter Space (Forthcoming/Available Online)
Authors: O'Hare, Anthony
Contact Email: anthony.ohare@stir.ac.uk
Keywords: bayesian
inference
Adaptive Metropolis algorithm
Monte Carlo
epidemiology
likelihood
Markov Chain
Issue Date: 15-Jul-2015
Publisher: Mary Ann Liebert, Inc
Citation: O'Hare A Inference in High Dimensional Parameter Space (Forthcoming/Available Online), Journal of Computational Biology.
Abstract: 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.
Type: Journal Article
URI: http://hdl.handle.net/1893/22224
DOI Link: http://dx.doi.org/10.1089/cmb.2015.0086
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Affiliation: Complex Systems

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