dc.contributor.authorYu, Hang
dc.contributor.authorChoo, Zheng
dc.contributor.authorDauwels, Justin
dc.contributor.authorJonathan, Philip
dc.contributor.authorZhou, Qiao
dc.identifier.citationYu, H., Choo, Z., Dauwels, J., Jonathan, P., & Zhou, Q. (2012). Modeling spatially-dependent extreme events with Markov random field priors. 2012 IEEE International Symposium on Information Theory - ISIT, pp.1453-1457.
dc.description.abstractA novel spatial model for extreme events is proposed. The model may for instance be used to describe the occurrence of catastrophic events such as earthquakes, floods, or hurricanes in certain regions; it may therefore be relevant for, e.g., weather forecasting, urban planning, and environmental assessment. The model is derived from the following ideas: The above-threshold values at each location are assumed to follow a generalized Pareto (GP) distribution. The GP parameters are coupled across space through Markov random fields, in particular, thin-membrane models. The latter are inferred through an empirical Bayes approach. Numerical results are presented for synthetic and real data (related to hurricanes in the Gulf of Mexico).en_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering
dc.titleModeling spatially-dependent extreme events with Markov random field priorsen_US
dc.typeConference Paper
dc.contributor.conferenceIEEE International Symposium on Information Theory (2012 : Cambridge, US)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US

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