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:
- 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.
- (Coming Soon) Alternating Direction Method of Multipliers (ADMM) for MAP inference in MRFs.
- 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.