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


RESULTS
The 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.


CONCLUSIONS
Mocapy++ 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.
Bioinformatics Centre, University of Copenhagen, Denmark. palu@binf.ku.dk
Martin
M
Paluszewski
Thomas
T
Hamelryck
D001499:Bayes Theorem; D015233:Models, Statistical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry; D012313:RNA / Q000737:chemistry; D012984:Software
D011506:Proteins; D012313:RNA