How to use the code

The documentation for DGM consists of a series of demos, showing how to use DGM to perform various tasks. These demos also contain some tutorial material on graphical models.

Demo 1D : An introduction to graphical models and to the tasks of inference and decoding on a set of simple examples:
- Exact : An introduction to graphical models and the tasks of decoding and inference on a small graphical model where we can do everything by hand.
- Chain : An introduction to Markov independence properties on an example of a chain-structured graphical model, and to efficient dynamic programming algorithms for inference.
- Tree : This demo shows how to construct a tree-structured graphical model, for which also an exact message-passing inference algorithm exists.
Demo 2D : An example of more complicated graphical models, containing loops and built upon a binary 2-dimentional image. This example also shows the application of DGM to unsupervised segmentation.
Demo Stereo : An example of CRFs application to the problem of disparity estimation between a pair of stereo images.
Demo Feature Extraction : An introduction to the feature extraction, needed mainly for supervised learning.
Demo Train : An introdiction to the random model learning (training) in case when the training data is available.
Demo Visualization : An example of usage the visualization module of the library for analysis and represention of the intermediate and final results of classification.