This work considers the low‐rank approximation of a matrix A (t) A (t) depending on a
parameter tt in a compact set D⊂ ℝ d D ⊂ R^ d. Application areas that give rise to such …

Computing low-rank approximations of kernel matrices is an important problem with many
applications in scientific computing and data science. We propose methods to efficiently …

We analyse the calibration of BayesCG under the Krylov prior. BayesCG is a probabilistic
numeric extension of the Conjugate Gradient (CG) method for solving systems of linear …

In image reconstruction, an accurate quantification of uncertainty is of great importance for
informed decision making. Here, the Bayesian approach to inverse problems can be used …

Various applications in numerical linear algebra and computer science are related to
selecting the r× r submatrix of maximum volume contained in a given matrix A∈ R n× n. We …

In this thesis we will present and analyze randomized algorithms for numerical linear
algebra problems. An important theme in this thesis is randomized low-rank approximation …

Gaussian random fields play an important role in many areas of science and engineering. In
practice, they are often simulated by sampling from a high-dimensional multivariate normal …

We consider the $\mathcal {H}^ 2$-formatted compression and computational estimation of
covariance functions on a compact set in $\mathbb {R}^ d $. The classical sample …

Linear algebra is a fundamental component of pure mathematics. It also lies at the heart of
many scientific, engineering, and industrial applications. Research and development in …