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DTSTART:20210314T070000
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DTSTART:20201101T060000
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DTSTART;TZID=America/Toronto:20211104T160000
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URL:https://uwaterloo.ca/statistics-and-actuarial-science/events/david-spro
 tt-distinguished-lecture-christian-genest
SUMMARY:David Sprott Distinguished Lecture by Christian Genest
CLASS:PUBLIC
DESCRIPTION:Please Note: This seminar will be given online.\n\nBAYESIAN HI
 ERARCHICAL MODELING OF SPATIAL EXTREMES\n\n-------------------------\n\nCl
 imate change and global warming have increased the need to assess\nand for
 ecast environmental risk over large domains and to develop\nmodels for the
  extremes of natural phenomena such as droughts\,\nfloods\, torrential pr
 ecipitation\, and heat waves. Because\ncatastrophic events are rare and ev
 idence is limited\, Bayesian methods\nare well suited for the areal analys
 is of their frequency and size. In\nthis talk\, a multi-site modeling stra
 tegy for extremes will be\ndescribed in which spatial dependence is captur
 ed through a latent\nGaussian random field whose behavior is driven by sy
 nthetic\ncovariates from climate reconstruction models. It will be seen th
 rough\ntwo vignettes that the site-to-site information sharing mechanism\n
 built into this approach does not only generally improve inference at\nany
  location but also allows for smooth interpolation over large\,\nsparse do
 mains.\n\nThe first application will concern the quantification of the\n
 magnitude of extreme surges on the Atlantic coast of Canada as part of\nth
 e development of an overland flood protection product by an\ninsurance co
 mpany. The second illustration will show how coherent\nestimates of extrem
 e precipitation of several durations based on a\nBayesian hierarchical spa
 tial model enhance current methodology for\nthe construction\, at monitor
 ed and unmonitored locations\, of IDF\ncurves commonly used in infrastruct
 ure design\, flood protection\, and\nurban drainage or water management.\
 n\n-------------------------
DTSTAMP:20260410T173222Z
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