Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes

Linear Parameter Uncertainty Quantification using Surrogate Gaussian Processes

We consider uncertainty quantification using surrogate Gaussian processes. We take a previous sampling algorithm and provide a closed form expression of the resulting posterior distribution. We extend the method to weighted least squares and a Bayesian approach both with closed form expressions of t...

Full description

Bibliographic Details
Main Author: Macatula, Romcholo Yulo
Other Authors: Mathematics
Format: Others
Published: Virginia Tech 2020
Subjects:
uncertainty quantification
surrogate models
linear parameter estimation
tomography
bayesian
gaussian process
Online Access:http://hdl.handle.net/10919/99411

Internet

http://hdl.handle.net/10919/99411

Similar Items