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pro vyhledávání: '"Nielsen, Thomas D"'
Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining
Externí odkaz:
http://arxiv.org/abs/2411.02125
Publikováno v:
In Journal of Logical and Algebraic Methods in Programming October 2024 141
Autor:
Goorden, Martijn A., Larsen, Kim G., Nielsen, Jesper E., Nielsen, Thomas D., Qian, Weizhu, Rasmussen, Michael R., Srba, Jiří, Zhao, Guohan
Publikováno v:
In Nonlinear Analysis: Hybrid Systems August 2024 53
Autor:
Goorden, Martijn A., Larsen, Kim G., Nielsen, Jesper E., Nielsen, Thomas D., Rasmussen, Michael R., Srba, Jiri
Storm water detention ponds are used to manage the discharge of rainfall runoff from urban areas to nearby streams. Their purpose is to reduce the hydraulic impact and sediment loads of the receiving waters. Detention ponds are currently designed bas
Externí odkaz:
http://arxiv.org/abs/2104.12509
Autor:
Jensen, Anne E., Bjørn, Siska, Nielsen, Thomas D., Moriggl, Bernhard, Hoermann, Romed, Vaeggemose, Michael, Bendtsen, Thomas F.
Publikováno v:
In Journal of Clinical Anesthesia February 2024 92
Recent advances in statistical inference have significantly expanded the toolbox of probabilistic modeling. Historically, probabilistic modeling has been constrained to (i) very restricted model classes where exact or approximate probabilistic infere
Externí odkaz:
http://arxiv.org/abs/1908.03442
Autor:
Masegosa, Andres, Nielsen, Thomas D., Langseth, Helge, Ramos-Lopez, Dario, Salmeron, Antonio, Madsen, Anders L.
Making inferences from data streams is a pervasive problem in many modern data analysis applications. But it requires to address the problem of continuous model updating and adapt to changes or drifts in the underlying data generating distribution. I
Externí odkaz:
http://arxiv.org/abs/1707.02293
Autor:
Masegosa, Andrés R., Martínez, Ana M., Ramos-López, Darío, Cabañas, Rafael, Salmerón, Antonio, Nielsen, Thomas D., Langseth, Helge, Madsen, Anders L.
The AMIDST Toolbox is a software for scalable probabilistic machine learning with a spe- cial focus on (massive) streaming data. The toolbox supports a flexible modeling language based on probabilistic graphical models with latent variables and tempo
Externí odkaz:
http://arxiv.org/abs/1704.01427
Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we determine a set of strategies for the decisions involved. A strategy for a decision variable is in principle a function over i
Externí odkaz:
http://arxiv.org/abs/1301.6729
When using Bayesian networks for modelling the behavior of man-made machinery, it usually happens that a large part of the model is deterministic. For such Bayesian networks deterministic part of the model can be represented as a Boolean function, an
Externí odkaz:
http://arxiv.org/abs/1301.3880