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Abstract Details
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Activity Number:
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470
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #301753 |
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Title:
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MCMC Methods Using Temporary Mapping and Caching and Its Applications on Gaussian Process Regression
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Author(s):
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Chunyi Wang*+ and Radford M. Neal
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Companies:
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University of Toronto and University of Toronto
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Address:
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100 St George Street, Room 6003, Toronto, ON, M5S2M8, Canada
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Keywords:
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MCMC ;
Gaussian Process
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Abstract:
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We propose two general ideas for constructing efficient MCMC methods - temporarily mapping to a new space, which can be either larger or smaller than the original space, and caching the results of previous computations for future re-use. These two ideas can be combined to improve efficiency for a wide range of problems, examples include: problems where probabilities can be quickly recomputed when only some 'fast' variables are changed; effectively adapt tuning parameters without changing the Markov Chain transition (effectively adapting but not really adapting). We demonstrate the effectiveness of these methods by its applications on the Gaussian Progress Regression.
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