Minimum Discrepancy Methods in Uncertainty Quantification

09/13/2021
by   Chris J. Oates, et al.
Newcastle University
0

The lectures were prepared for the École Thématique sur les Incertitudes en Calcul Scientifique (ETICS) in September 2021.

READ FULL TEXT
02/08/2023

Fortuna: A Library for Uncertainty Quantification in Deep Learning

We present Fortuna, an open-source library for uncertainty quantificatio...
12/17/2020

Uncertainty Quantification in Case of Imperfect Models: A Review

Uncertainty quantification of complex technical systems is often based o...
12/05/2018

Empirical priors and coverage of posterior credible sets in a sparse normal mean model

Bayesian methods provide a natural means for uncertainty quantification,...
03/01/2021

Uncertainty Quantification by Ensemble Learning for Computational Optical Form Measurements

Uncertainty quantification by ensemble learning is explored in terms of ...
10/22/2017

Bayesian uncertainty quantification for epidemic spread on networks

While there exist a number of mathematical approaches to modeling the sp...
02/05/2023

Direct Uncertainty Quantification

Traditional neural networks are simple to train but they produce overcon...
10/06/2020

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures

Uncertainty quantification for complex deep learning models is increasin...

Please sign up or login with your details

Forgot password? Click here to reset