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Modeling spatially-dependent extreme events with Markov random field priors

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Modeling spatially-dependent extreme events with Markov random field priors. / Yu, H.; Choo, Z.; Dauwels, J.; Jonathan, P.; Zhou, Q.

2012 IEEE International Symposium on Information Theory Proceedings. IEEE, 2012. p. 1453-1457.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Yu, H, Choo, Z, Dauwels, J, Jonathan, P & Zhou, Q 2012, Modeling spatially-dependent extreme events with Markov random field priors. in 2012 IEEE International Symposium on Information Theory Proceedings. IEEE, pp. 1453-1457. https://doi.org/10.1109/ISIT.2012.6283503

APA

Yu, H., Choo, Z., Dauwels, J., Jonathan, P., & Zhou, Q. (2012). Modeling spatially-dependent extreme events with Markov random field priors. In 2012 IEEE International Symposium on Information Theory Proceedings (pp. 1453-1457). IEEE. https://doi.org/10.1109/ISIT.2012.6283503

Vancouver

Yu H, Choo Z, Dauwels J, Jonathan P, Zhou Q. Modeling spatially-dependent extreme events with Markov random field priors. In 2012 IEEE International Symposium on Information Theory Proceedings. IEEE. 2012. p. 1453-1457 https://doi.org/10.1109/ISIT.2012.6283503

Author

Yu, H. ; Choo, Z. ; Dauwels, J. ; Jonathan, P. ; Zhou, Q. / Modeling spatially-dependent extreme events with Markov random field priors. 2012 IEEE International Symposium on Information Theory Proceedings. IEEE, 2012. pp. 1453-1457

Bibtex

@inproceedings{af1273c9905f4b8f81fb9b82c4c238d9,
title = "Modeling spatially-dependent extreme events with Markov random field priors",
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). {\textcopyright} 2012 IEEE.",
keywords = "Catastrophic event, Empirical Bayes approach, Environmental assessment, Extreme events, Gulf of Mexico, Markov Random Fields, Numerical results, Spatial models, Synthetic and real data, Hurricanes, Information theory, Urban planning, Weather forecasting, Image segmentation",
author = "H. Yu and Z. Choo and J. Dauwels and P. Jonathan and Q. Zhou",
year = "2012",
doi = "10.1109/ISIT.2012.6283503",
language = "English",
isbn = "9781467325806",
pages = "1453--1457",
booktitle = "2012 IEEE International Symposium on Information Theory Proceedings",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Modeling spatially-dependent extreme events with Markov random field priors

AU - Yu, H.

AU - Choo, Z.

AU - Dauwels, J.

AU - Jonathan, P.

AU - Zhou, Q.

PY - 2012

Y1 - 2012

N2 - 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). © 2012 IEEE.

AB - 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). © 2012 IEEE.

KW - Catastrophic event

KW - Empirical Bayes approach

KW - Environmental assessment

KW - Extreme events

KW - Gulf of Mexico

KW - Markov Random Fields

KW - Numerical results

KW - Spatial models

KW - Synthetic and real data

KW - Hurricanes

KW - Information theory

KW - Urban planning

KW - Weather forecasting

KW - Image segmentation

U2 - 10.1109/ISIT.2012.6283503

DO - 10.1109/ISIT.2012.6283503

M3 - Conference contribution/Paper

SN - 9781467325806

SP - 1453

EP - 1457

BT - 2012 IEEE International Symposium on Information Theory Proceedings

PB - IEEE

ER -