Graphical models provide a compact interpretable representation of complex statistical phenomena by encoding random variables as vertices in a graph and relationships between those variables as edges. The Graphical Models toolkit provides a collection of methods to make predictions under uncertainty, and for reasoning about structured noisy data.

The main components of Graphlab Graphical Models toolkit are:

1. Distributed Dual Decomposition: performs maximum a posteriori (MAP) inference in general Markov Random Fields via the Dual Decomposition algorithm. The MRF is assumed to be provided in the standard UAI file format. Maintained by Dhruv Batra.
2. (Coming Soon) Alternating Direction Method of Multipliers (ADMM) for MAP inference in MRFs.
3. Structured Prediction: that applies the Loopy Belief propagation (LBP) algorithm to a pair-wise Markov Random Field encoding the classic Potts Model.

See the documentation for further details.