Direct Graphical Models
v.1.5.2

is a C++ dynamic link library implementing various tasks in probabilistic graphical models with pairwise dependencies. The library aims to be used for the Markov and Conditional Random Fields (MRF / CRF), Markov Chains, Bayesian Networks, etc. DGM library consists of three modules:
These tasks are optimized for speed, i.e. highefficient calculations. The code is written entirely in C++ and can be compiled with Microsoft Visual C++.
DGM implements the following training methods:
The corresponding classes are CTrainNode* (where * is the name of the method above). The difference between these methods is described at forum: Training of a Random Model.
The corresponding classes are CTrainEdge* (where * is the name of the method above).
DGM implements the following inference and decoding methods:
The corresponding classes are CInfer* (where * is the name of the method above).
All of the inference classes may be also used for approximate decoding via function DirectGraphicalModels::CInfer::decode()
The corresponding classes are CDecode* (where * is the name of the method above).
DGM implements the following parameter estimation method:
DGM implements the following sampling method:
Please refer to the FEX Module documentation
Please refer to the VIS Module documentation