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Abstract Details
Activity Number:
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322
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Type:
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Invited
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #300314 |
Title:
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Computationally Feasible Hierarchical Modeling Strategies for Large Spatial Data Sets
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Author(s):
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Rajarshi Guhaniyogi*+ and Sudipto Banerjee
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Companies:
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University of Minnesota and University of Minnesota
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Address:
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A460 MAYO BUILDING, MAIL CODE 303, MINNEAPOLIS, MN, MN55455,
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Keywords:
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Bayesian modeling ;
Low-rank Gaussian processes ;
Hierarchical modeling ;
Markov chain Monte Carlo ;
Spatial data ;
Spatial super-populations
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Abstract:
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Large point referenced datasets are common in the environmental and natural sciences. The computational burden in fitting large spatial datasets undermines estimation of Bayesian models. We explore several improvements low-rank and other scalable spatial process models including reduction of biases and process-based modeling of ``centers'' or ``knots'' that determine optimal subspaces for data projection. We also consider alternate strategies for handling massive spatial datasets. One approach concerns developing process-based super-population models and developing Bayesian finite-population sampling techniques for spatial data. We also explore model-based simultaneous dimension-reduction in space, time and the number of variables. Flexible and rich hierarchical modeling applications in forestry are demonstrated.
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Authors who are presenting talks have a * after their name.
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