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Found 3 papers, 1 papers with code
Spatiotemporal Besov Priors for Bayesian Inverse Problems
Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast.
Bayesian Learning via Q-Exponential Process
In this work, we generalize the $q$-exponential distribution (with density proportional to) $\exp{(- \frac{1}{2}|u|^q)}$ to a stochastic process named $Q$-exponential (Q-EP) process that corresponds to the $L_q$ regularization of functions.
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks
To address this issue, several methods based on surrogate models have been proposed to speed up the inference process.
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