Graph-Constrained Correlation Dynamics

Graph-Constrained Correlation Dynamics allows the specification of
models that describe probability
distributions which evolve continuously in time according to the
chemical master equation.
This is accomplished by combining a
Markov Random Field, which represents the instantaneous
probability of the system, with a set
of ordinary differential equations on the parameters of the MRF.

GCCDdd is an implementation of GCCD within the*Mathematica*
programming language, available
here. This package also includes
two example GCCD models.
GCCDdd is offered with the
GNU Public License; essentially you are free to create
derivative works, as long as they too carry a license as open
as the GPL.

In order to use GCCDdd, you will need the Depenency Diagrams package, which is available here.

Additionally, the GCCD package implements an alternative learning algorithm for GCCD. This method is based on fitting parameters for the time-derivates of MRF parameters using the lasso.

References:

Todd Johnson, "Dependency Diagrams and Graph-Constrained Correlation Dynamics: New Systems for Probabilistic Graphical Modeling," PhD Thesis, University of California, Irvine, March 2012.

GCCDdd is an implementation of GCCD within the

In order to use GCCDdd, you will need the Depenency Diagrams package, which is available here.

Additionally, the GCCD package implements an alternative learning algorithm for GCCD. This method is based on fitting parameters for the time-derivates of MRF parameters using the lasso.

References:

Todd Johnson, "Dependency Diagrams and Graph-Constrained Correlation Dynamics: New Systems for Probabilistic Graphical Modeling," PhD Thesis, University of California, Irvine, March 2012.