Linzner, Dominik and Schmidt, Michael and Koeppl, Heinz (2019):
Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data.
In: Proceedings of Neural Information Processing Systems, In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 09.12.-13.12.2019, [Online-Edition: https://arxiv.org/abs/1909.04570],
[Conference or Workshop Item]

Schmidt, Michael and Hamacher, Kay (2018):
hoDCA: higher order direct-coupling analysis.
In: BMC bioinformatics, p. 546, 19, (1), ISSN 1471-2105,
[Article]

Wegener, Moritz and Spiehl, Dieter and Sauer, Hans Martin and Mikschl, Florian and Liu, Xinxin and Kölpin, Nadja and Schmidt, Michael and Jank, Michael and Dörsam, Edgar and Roosen, Andreas (2016):
Flexographic printing of nanoparticulate tin-doped indium oxide inks on PET foils and glass substrates.
In: Journal of Materials Science, Springer US, pp. 4588-4600, 51, (9), ISSN 0022-2461,
[Online-Edition: http://dx.doi.org/10.1007/s10853-016-9772-3],
[Article]

Eichhorn, Andreas
Kutterer, Hansjörg and Seitz, Florian and Alkhatib, Hamza and Schmidt, Michael (eds.) (2015):
Parametric Modeling of Static and Dynamic Processes in Engineering Geodesy.
In: The 1st International Workshop on the Quality of Geodetic Observation and Monitoring Systems (QuGOMS'11), Springer International Publishing, pp. 117-125, [Online-Edition: http://link.springer.com/chapter/10.1007/978-3-319-10828-5_1...],
[Book Section]