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SUMMARY:A Comparison of Approximate Bayesian Computation and Stochastic Ca
 libration for Spatio-Temporal Models of High-Frequency Rainfall Patterns -
  Matthew Pratola (Ohio State University)
DTSTART:20180412T143000Z
DTEND:20180412T150000Z
UID:TALK103741@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Modeling complex environmental phenomena such as rainfall patt
 erns has proven challenging due to the difficulty in capturing heavy-taile
 d behavior\, such as extreme weather\, in a meaningful way.  Recently\, a 
 novel approach to this task has taken the form of so-called stochastic wea
 ther generators\, which use statistical formulations to emulate the distri
 butional patterns of an environmental process.  However\, while sampling f
 rom such models is usually feasible\, they typically do not possess closed
 -form likelihood functions\, rendering the usual approaches to model fitti
 ng infeasible.  Furthermore\, some of these stochastic weather generators 
 are now becoming so complex that even simulating from them can be computat
 ionally expensive.  We propose and compare two approaches to fitting compu
 tationally expensive stochastic weather generators motivated by Approximat
 e Bayesian Computation and Stochastic Simulator Calibration methodologies.
   The methods are then demonstrated by estimating important parameters of 
 a recent stochastic weather generator model applied to rainfall data from 
 the continental USA.
LOCATION:Seminar Room 1\, Newton Institute
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