Munk, Axel

Authors König C, Munk A, Werner F Journal Journal of the Royal Statistical Society Series B: Statistical Methodology Citation J R Stat Soc Series B: Statistical Methodology, 2024, 00, 1–19. Abstract We develop a multiscale scanning method to find anomalies in a d-dimensional random field in the presence of nuisance

Authors Naas J, Nies G, Li H, Stoldt S, Schmitzer B, Jakobs S, Munk A Journal Communications Biology Citation Commun Biol 7, 1139 (2024). Abstract With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate

Authors Kovacs S, Li H, Haubner L, Munk A, Buhlmann P Journal Journal of Machine Learning Research Citation J Mach Learn Res 25(297):1−64, 2024. Abstract Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching one

Authors Hundrieser S, Mordant G. Weitkamp CA, Munk A Journal Stochastic Processes and their Applications Citation Stochastic Processes and their Applications 178 (2024) 104462. Abstract Optimal transport (OT) based data analysis is often faced with the issue that the underlying cost function is (partially) unknown. This is addressed in this

Authors Suchan L, Li H, Munk A Journal Arxiv Citation arXiv:2407.15297. Abstract Graph cuts are among the most prominent tools for clustering and classification analysis. While intensively studied from geometric and algorithmic perspectives, graph cut-based statistical inference still remains elusive to a certain extent. Distributional limits are fundamental in understanding

Authors Vanegas LJ, Eltzner B, Rudolf D, Dura M, Lehnart SE, Munk A Journal The Annals of Applied Statistics Citation Ann. Appl. Stat. 18(2): 1445-1470. Abstract We propose and investigate a hidden Markov model (HMM) for the analysis of dependent, aggregated, superimposed two-state signal recordings. A major motivation for this

Authors Hundrieser S, Staudt T, Munk A Journal Annales de l’Institut Henri Poincare (B) Probabilites et statistiques Citation Ann. Inst. H. Poincaré Probab. Statist. 60(2): 824-846. Abstract The empirical optimal transport (OT) cost between two probability measures from random data is a fundamental quantity in transport based data analysis. In

Authors Hundrieser S, Klatt M, Munk A Journal The Annals of Applied Probability Citation Ann. Appl. Probab. 34(1B): 1403-1468. Abstract For probability measures on countable spaces we derive distributional limits for empirical entropic optimal transport quantities. More precisely, we show that the empirical optimal transport plan weakly converges to a

Authors Mémoli F, Munk A, Wan Z, Weitkamp C Journal Discrete & Computational Geometry Citation Discrete Comput Geom (2023) Abstract We investigate compact ultrametric measure spaces which form a subset Uw of the collection of all metric measure spaces Mw. In analogy with the notion of the ultrametric Gromov–Hausdorff distance

Authors Suchan L, Li H, Munk A Journal Arxiv Citation arXiv:2308.09613. Abstract Due to its computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original graph cuts such as Ratio, Normalized or Cheeger Cut in order