Poster
in
Workshop: Machine Learning for Engineering Modeling, Simulation and Design
Bayesian polynomial chaos
Pranay Seshadri · Andrew Duncan · Ashley Scillitoe
Abstract:
In this brief paper we introduce Bayesian polynomial chaos, a Gaussian process analogue to polynomial chaos. We argue why this Bayesian re-formulation of polynomial chaos is necessary and then proceed to mathematically define it, followed by an examination of its utility in computing moments and sensitivities; multi-fidelity modelling, and information fusion.
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