Direct Graphical Models
v.1.7.0

is a C++ dynamic link library implementing various tasks in probabilistic graphical models with pairwise dependencies as well as complete (dense) graphs. 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