Activity Number:
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422
- SPEED: Section on Bayesian Statistical Science
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 2:00 PM to 2:45 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #325376
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Title:
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Partioning Priors for Spatiotemporal Multiscale Models
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Author(s):
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Andrew Hoegh*
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Companies:
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Montana State University
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Keywords:
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Abstract:
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In recent years, multiscale models have become popular for modeling spatiotemporal phenomenon. One advantage of these models is that the computation is scalable for a given multiscale partition. The specified partition controls the spatial structure in the model. In many applications there may be problem specific reasons to choose a particular multiscale partition; however, there may be more appropriate spatial partitions for a given problem. We propose a prior that enables a mixture of multiscale partitions or a procedure for choosing the maximum a posteriori partition.
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Authors who are presenting talks have a * after their name.
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