Munk, Axel

Authors Steinkamp F, Rodríguez LA, Otte JV, Munk A Journal Arxiv Citation arXiv:2609.11670. Abstract We introduce statistical theory for the matching of finitely many objects, represented as metric measure spaces (mm-spaces). The approach is based on the second lower bound (SLB) of the Gromov-Wasserstein distance and thus is able to

Authors Gaubert M, Alachram H, Gönenç II, Domínguez I, Argyriou C, Schmidt J, Kaulfuß S, Pavez-Giani M, Schott CT, Munk A, Zibat A, Cyganek L, Yigit G, Wollnik B Journal BioRxiv Citation bioRxiv 2026.08.18.745450. Abstract The human heart has a continuous and exceptionally high demand for energy, which is met

Authors Arenas-Velilla S, Munk A, Rodríguez LA Journal Arxiv Citation arXiv:2604.12940 Abstract Recently, the statistical properties of empirical Entropic Optimal Transport (EOT) have attracted great interest, as this quantity has been shown to be useful for complex data analysis, among other reasons due to its computational efficiency. In several applications,

Authors Struleva M, Hundrieser S, Schuhmacher D, Munk A Journal Stochastic Processes and their Applications Citation Stochastic Processes and their Applications 198 (2026) 104938. Abstract We statistically analyze empirical plug-in estimators for unbalanced optimal transport (UOT) formalisms, focusing on the Kantorovich–Rubinstein distance, between general intensity measures based on observations from

Authors Mösching A, Li H, Munk A Journal Journal of Computational and Graphical Statistics Citation Journal of Computational and Graphical Statistics, 1–15. Abstract Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process—a noisy version of the hidden chain. Decoding the original signal from the

Authors Li H, Liu Z, Munk A Journal IEEE Transactions on Information Theory Citation EEE Transactions on Information Theory, 72, 2, 1240-1275, Feb. 2026. Abstract Modern large-scale data analysis increasingly faces the challenge of achieving computational efficiency as well as statistical accuracy, as classical statistically efficient methods often fall short

Authors Suchan L, Li H, Munk A Journal Electronic Journal of Statistics Citation Electron. J. Statist. 19(2): 5925-5978 (2025). 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

Authors Hüselitz A, Li H, Munk A Journal Journal of Time Series Analysis Citation Journal of Time Series Analysis 1–22. Abstract We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM-type statistics

Authors Requadt R, Li H, Munk A Journal Arxiv Citation arXiv:2510.05645. Abstract We investigate the asymptotic behavior of parametric Bayes estimators under a broad class of loss functions that extend beyond the classical translation-invariant setting. To this end, we develop a unified theoretical framework for loss functions exhibiting locally polynomial

Authors Groppe M, Niemöller L, Hundrieser S, Ventzke D, Blob A, Köster S, Munk A Journal Arxiv Citation arXiv:2509.13970. Abstract We introduce a general framework for testing statistical hypotheses for probability measures supported on finite spaces, which is based on optimal transport (OT). These tests are inspired by the analysis