Abstract Details
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Activity Number:
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569
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Type:
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Invited
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #307073 |
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Title:
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Likelihood Approximation for Large Environmental Data Sets
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Author(s):
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Michael L Stein*+
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Companies:
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The University of Chicago
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Keywords:
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Composite likelihood ;
Spatial statistics ;
Numerical methods
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Abstract:
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This talk will discuss methods for fitting Gaussian process models to large environmental datasets, both spatial and spatial-temporal, with a particular focus on ungridded data. The talk will consider approximations to the likelihood, such as composite likelihood, that reduce computations, and methods from numerical analysis, such as preconditioned conjugate gradient, that can reduce the memory and calculations required to compute exact or approximate likelihoods. Comparisons to alternative approaches to reducing computations, such as low rank methods, will be given.
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Authors who are presenting talks have a * after their name.
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