Non-Linear Approximation of Bayesian Update

Abstract
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.

Acknowledgements
SRI UQ, ECRC KAUST

Conference/Event Name
ECRC Meeting

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