Mocapy++ - a toolkit for inference and learning in dynamic Bayesian networks
Research output: Contribution to journal › Journal article › Research › peer-review
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
APA
Vancouver
Author
Bibtex
}
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