Publications:

• M. Behr and A. Munk, Identifiability for blind source separation of multiple finite alphabet linear mixtures, IEEE Transactions on Information Theory, 63(9):5506-5517 (2017)
• M. Behr, C. Holmes, and A. Munk, Multiscale blind source separation, The Annals of Statistics, 46(2):711–744 (2018)
• M. Behr, Finite Alphabet Blind Separation, PhD thesis, University of Göttingen (2018)
• M. Behr, M. Azim Ansari, A. Munk, C. Holmes, Testing for dependence on tree structures, Proceedings of the National Academy of Sciences (2020)

• L. Vanegas, M. Behr, and A. Munk, Multiscale Quantile Segmentation, to appear in the Journal of the American Statistical Association (2020) arXiv:1902.09321

Preprints:

• M. Behr ^*, K. Kumbier^*, A. Cordova-Palomera, M. Aguirre, E. Ashley, A. J. Butte, R. Arnaout, B. Brown, J. Priest, B. Yu, Learning epistatic polygenic phenotypes with Boolean interactions, bioRxiv preprint

• M. Pelizzola^*, M. Behr^*, H. Li, A. Munk, and A. Futschik, Multiple Haplotype Reconstruction from Allele Frequency Datan, bioRxiv preprint

• M. Behr and A. Munk, Minimax estimation in linear models with unknown design over finite alphabets, arXiv:1711.04145

Software:

• R-package slamR,http://www.stochastik.math.uni-goettingen.de/slamR
• R-package and Jupyter Notebook treeSeg,
  https://github.com/merlebehr/treeSeg
• R-package haploSep, (together with M. Pelizzola and H. Li)
  https://github.com/MartaPelizzola/haploSep
• R-package and Jupyter Notebook mqs, (together with L. Vanegas)
  https://github.com/ljvanegas/mqs
• GitHub repository epiTree, (together with K. Kumbier)
  https://github.com/merlebehr/epiTree

^* joint first author