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Activity Number:
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119
- SPEED: Bayesian Methods Student Awards
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #322953
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View Presentation
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Title:
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Sensitivity Analysis and Emulation for Functional Data Using Bayesian Adaptive Splines
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Author(s):
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Devin Francom* and Bruno Sanso and Ana Kupresanin
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Companies:
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Lawrence Livermore National Laboratory and University of California Santa Cruz and Lawrence Livermore National Laboratory
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Keywords:
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nonparametric regression
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Abstract:
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We discuss the Bayesian approach to multivariate adaptive regression splines (BMARS) as an emulator for a computer model that outputs curves. We introduce modifications to traditional BMARS approaches that allow for fitting large amounts of data and allow for more efficient MCMC sampling. We emphasize the ease with which sensitivity analysis can be performed in this situation. We present a sensitivity analysis of a computer model of the deformation of a protective plate used in pressure driven experiments. Our example serves as an illustration of the ability of BMARS emulators to fulfill all the necessities of computability, flexibility and reliable calculation on relevant measures of sensitivity.
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Authors who are presenting talks have a * after their name.
