Macatula, Romcholo Yulo2020-07-242020-07-242020-07-21vt_gsexam:26965http://hdl.handle.net/10919/99411We 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 the resulting posterior distributions. We test methods on 1D deconvolution and 2D tomography. Our new methods improve on the previous algorithm, however fall short in some aspects to a typical Bayesian inference method.ETDIn Copyrightuncertainty quantificationsurrogate modelslinear parameter estimationtomographybayesiangaussian processThesis