M. Kiderlen

activity

20062021

most citedConvergence of algorithms for reconstructing convex bodies and directional measures

46 citations · 47 across the 2 of their papers we have counts for

collaborators

3 papers

math.NT2021★ 1 cited

On a partition with a lower expected $L_{2}$ -discrepancy than classical jittered sampling

Markus Kiderlen, Florian Pausinger

We prove that classical jittered sampling of the $d$ -dimensional unit cube does not yield the smallest expected $L_{2}$ -discrepancy among all stratified samples with $N=m^d…

math.ST2020

Discrepancy of stratified samples from partitions of the unit cube

Markus Kiderlen, Florian Pausinger

We extend the notion of jittered sampling to arbitrary partitions and study the discrepancy of the related point sets. Let $Ω = (Ω_{1}, \dots, Ω_{N})$ be a partition of $[0, 1]^{d}$ a…

math.ST2006★ 46 cited

Convergence of algorithms for reconstructing convex bodies and directional measures

Richard J. Gardner, Markus Kiderlen, Peyman Milanfar

We investigate algorithms for reconstructing a convex body $K$ in $R^{n}$ from noisy measurements of its support function or its brightness function in $k$ directions $u_1,…

On a partition with a lower expected L2​-discrepancy than classical jittered sampling

Discrepancy of stratified samples from partitions of the unit cube

Convergence of algorithms for reconstructing convex bodies and directional measures

On a partition with a lower expected $L_{2}$ -discrepancy than classical jittered sampling