3 citations · 3 across the 7 of their papers we have counts for
8 papers
Random spanning tree Markov random field priors for Bayesian inverse problems in imaging
Jasper Marijn Everink
Markov random fields are common prior distributions used in Bayesian inverse imaging problems. In particular, difference priors assign probability distributions to differences betw…
A Computational Framework and Implementation of Implicit Priors in Bayesian Inverse Problems
Jasper M. Everink, Chao Zhang, Amal M. A. Alghamdi +3
Solving Bayesian inverse problems typically involves deriving a posterior distribution using Bayes' rule, followed by sampling from this posterior for analysis. Sampling methods, s…
Fast Gaussian Processes under Monotonicity Constraints
Chao Zhang, Jasper M. Everink, Jakob Sauer Jørgensen
Gaussian processes (GPs) are widely used as surrogate models for complicated functions in scientific and engineering applications. In many cases, prior knowledge about the function…
Self-supervised Conformal Prediction for Uncertainty Quantification in Imaging Problems
Jasper M. Everink, Bernardin Tamo Amougou, Marcelo Pereyra
Most image restoration problems are ill-conditioned or ill-posed and hence involve significant uncertainty. Quantifying this uncertainty is crucial for reliably interpreting experi…
The Geometry and Well-Posedness of Sparse Regularized Linear Regression
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
In this work, we study the well-posedness of certain sparse regularized linear regression problems, i.e., the existence, uniqueness and continuity of the solution map with respect…
Sparse Bayesian Inference with Regularized Gaussian Distributions
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
Regularization is a common tool in variational inverse problems to impose assumptions on the parameters of the problem. One such assumption is sparsity, which is commonly promoted…
Bayesian Inference with Projected Densities
Jasper Marijn Everink, Yiqiu Dong, Martin Skovgaard Andersen
Constraints are a natural choice for prior information in Bayesian inference. In various applications, the parameters of interest lie on the boundary of the constraint set. In this…
Having Fun in Learning Formal Specifications
I. S. W. B. Prasetya, Craig Q. H. D. Leek, Orestis Melkonian +11
There are many benefits in providing formal specifications for our software. However, teaching students to do this is not always easy as courses on formal methods are often experie…