5 papers
Lattice Rules Meet Kernel Cubature
Vesa Kaarnioja, Ilja Klebanov, Claudia Schillings +1
Rank-1 lattice rules are a class of equally weighted quasi-Monte Carlo methods that achieve essentially linear convergence rates for functions in a reproducing kernel Hilbert space…
Construction of Optimal Algorithms for Function Approximation in Gaussian Sobolev Spaces
Yuya Suzuki, Toni Karvonen
This paper studies function approximation in Gaussian Sobolev spaces over the real line and measures the error in a Gaussian-weighted -norm. We construct two linear approximat…
Approximation in Hilbert spaces of the Gaussian and related analytic kernels
Toni Karvonen, Yuya Suzuki
We consider linear approximation based on function evaluations in reproducing kernel Hilbert spaces of certain analytic weighted power series kernels and stationary kernels on the…
Approximation of differential entropy in Bayesian optimal experimental design
Chuntao Chen, Tapio Helin, Nuutti Hyvönen +1
Bayesian optimal experimental design provides a principled framework for selecting experimental settings that maximize obtained information. In this work, we focus on estimating th…
Möbius-Transformed Trapezoidal Rule
Yuya Suzuki, Nuutti Hyvönen, Toni Karvonen
We study numerical integration by combining the trapezoidal rule with a Möbius transformation that maps the unit circle onto the real line. We prove that the resulting transformed…