Munk, Axel

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

Authors Pohlmann M, Werner F, Munk A Journal Information and Inference: A Journal of the IMA Citation Information and Inference: A Journal of the IMA, Volume 12, Issue 3, September 2023, iaad026. Abstract We investigate minimax testing for detecting local signals or linear combinations of such signals when only indirect

Authors Mordant G, Munk A Journal SIAM Journal on Mathematics of Data Science Citation IAM Journal on Mathematics of Data Science 2023 5:2, 528-557 Abstract In this paper, we consider a certain convolutional Laplacian for metric measure spaces and investigate its potential for the statistical analysis of complex objects. The

Authors Laitenberger O, Aspelmeier T, Staudt T, Geisler C, Munk A, Egner A Journal Nanomaterials Citation Nanomaterials 2023, 13(3), 459. Abstract With the advent of fluorescence superresolution microscopy, nano-sized structures can be imaged with a previously unprecedented accuracy. Therefore, it is rapidly gaining importance as an analytical tool in the

Authors Heinemann F, Klatt M, Munk A Journal Applied Mathematics & Optimization Citation Appl Math Optim 87, 4 (2023). Abstract The purpose of this paper is to provide a systematic discussion of a generalized barycenter based on a variant of unbalanced optimal transport (UOT) that defines a distance between general

Authors Kovács S, Li H, Bühlmann P, Munk A Journal Biometrika Citation Biometrika, 2022, asac052. Abstract We propose seeded binary segmentation for large scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of