Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/102586
Title: Modeling spatially-dependent extreme events with Markov random field priors
Authors: Yu, Hang
Choo, Zheng
Dauwels, Justin
Jonathan, Philip
Zhou, Qiao
Keywords: DRNTU::Engineering::Electrical and electronic engineering
DRNTU::Science::Mathematics
Issue Date: 2012
Source: Yu, 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.
Abstract: A 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).
URI: https://hdl.handle.net/10356/102586
http://hdl.handle.net/10220/16404
DOI: 10.1109/ISIT.2012.6283503
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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