Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks

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Standard

Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks. / Paluszewski, Martin; Hamelryck, Thomas Wim.

In: BMC Bioinformatics, Vol. 11, No. Suppl 1, 126, 2010.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Paluszewski, M & Hamelryck, TW 2010, 'Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks', BMC Bioinformatics, vol. 11, no. Suppl 1, 126. https://doi.org/10.1186/1471-2105-11-126

APA

Paluszewski, M., & Hamelryck, T. W. (2010). Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks. BMC Bioinformatics, 11(Suppl 1), [126]. https://doi.org/10.1186/1471-2105-11-126

Vancouver

Paluszewski M, Hamelryck TW. Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks. BMC Bioinformatics. 2010;11(Suppl 1). 126. https://doi.org/10.1186/1471-2105-11-126

Author

Paluszewski, Martin ; Hamelryck, Thomas Wim. / Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks. In: BMC Bioinformatics. 2010 ; Vol. 11, No. Suppl 1.

Bibtex

@article{c86e4100302b11df8ed1000ea68e967b,
title = "Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks",
abstract = "BackgroundMocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).ResultsThe program package is freely available under the GNU General Public Licence (GPL) from SourceForge (http://sourceforge.net/projects/mocapy). The package contains the source for building the Mocapy++ library, several usage examples and the user manual.ConclusionsMocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.",
author = "Martin Paluszewski and Hamelryck, {Thomas Wim}",
year = "2010",
doi = "10.1186/1471-2105-11-126",
language = "English",
volume = "11",
journal = "B M C Bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central Ltd.",
number = "Suppl 1",

}

RIS

TY - JOUR

T1 - Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks

AU - Paluszewski, Martin

AU - Hamelryck, Thomas Wim

PY - 2010

Y1 - 2010

N2 - BackgroundMocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).ResultsThe program package is freely available under the GNU General Public Licence (GPL) from SourceForge (http://sourceforge.net/projects/mocapy). The package contains the source for building the Mocapy++ library, several usage examples and the user manual.ConclusionsMocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.

AB - BackgroundMocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distributions, including distributions from directional statistics (the statistics of angles, directions and orientations).ResultsThe program package is freely available under the GNU General Public Licence (GPL) from SourceForge (http://sourceforge.net/projects/mocapy). The package contains the source for building the Mocapy++ library, several usage examples and the user manual.ConclusionsMocapy++ is especially suitable for constructing probabilistic models of biomolecular structure, due to its support for directional statistics. In particular, it supports the Kent distribution on the sphere and the bivariate von Mises distribution on the torus. These distributions have proven useful to formulate probabilistic models of protein and RNA structure in atomic detail.

U2 - 10.1186/1471-2105-11-126

DO - 10.1186/1471-2105-11-126

M3 - Journal article

C2 - 20226024

VL - 11

JO - B M C Bioinformatics

JF - B M C Bioinformatics

SN - 1471-2105

IS - Suppl 1

M1 - 126

ER -

ID: 18651977