Activity Number:
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485
- Highlights from Bayesian Analysis
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Type:
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Invited
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Date/Time:
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Wednesday, August 2, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #322191
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View Presentation
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Title:
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Solution Uncertainty Quantification for Differential Equations
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Author(s):
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Oksana A Chkrebtii* and David A Campbell and Ben Calderhead and Mark A Girolami
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Companies:
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The Ohio State University and Simon Fraser University and Imperial College London and University of Warwick
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Keywords:
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Bayesian numerical analysis ;
uncertainty quantification ;
differential equation models ;
Gaussian processes ;
Bayesian inference
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Abstract:
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When models are defined implicitly by systems of differential equations without a closed form solution, small local errors in finite-dimensional solution approximations can propagate into large deviations from the true underlying state trajectory. Inference for such models relies on a likelihood approximation constructed around a numerical solution, which underestimates posterior uncertainty. This talk will introduce and discuss progress in a new formalism for modeling and propagating discretization uncertainty through the Bayesian inferential framework, allowing exact inference and uncertainty quantification for discretized differential equation models.
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Authors who are presenting talks have a * after their name.