MCMC: A demo showing how to use Gibbs sampling for approximate sampling, and how to use sampling methods for approximate decoding/inference.ICM: A demo showing how to use the iteratedĬonditional mode algorithm (and other local search algorithms) for approximate decoding.The second set of demos covers approximate decoding/inference/sampling methods: GraphCuts: An example of a complicated loopy UGM, where the use of sub-moodular potentials (over binary data) allows us to perform exact decoding.Junction: A more complicated loopy UGM, where we take advantage of the low treewidth of the graph structure to perform exact decoding/inference/sampling.Cutset: Two examples of simple loopy UGMs, where we take advantage of the simplified graph structure after conditioning to perform exact decoding/inference/sampling.Condition: A demo that shows how we canĭo conditional decoding/inference/sampling, if we know the values of some.Tree: A tree-structured UGM, where dynamic.Chain: A chain-structured UGM, illustrating how the Markov independence properties present in the chain lead to efficient dynamic programming algorithms for decoding/inference/sampling.This tutorial also introduces the edgeStruct used by all the codes. Small: An introduction to UGMs and the tasks of decoding/inference/sampling on a a simple UGM where we can do everything by hand.The first set of demos covers exact decoding/inference/sampling: In Summer 2015 we also did a crash course on UGMs and the material from these courses is available here. These demos also contain some tutorial material on undirected graphical models. The documentation for UGM consists of a series of demos, showing how to use UGM to perform various tasks. The code is written entirely in Matlab, although moreĮfficient mex versions of many parts of the code are also available.ĭocumentation and Tutorial on Markov Random Fields and Conditional Random Fields Markov random fields and conditional random fields with log-linear potentials. The first three tasks are implemented for arbitrary discrete undirected graphical Training: Fitting a model to a given dataset.Sampling: Generating samples from the distribution.Inference: Computing the partition function and marginal probabilities. Decoding: Computing the most likely configuration.Specifically, it implements a variety of methods for the following four tasks: UGM is a set of Matlab functions implementing various tasks in probabilistic undirected graphical models of discrete data with pairwise (and unary) potentials. For probabilistic undirected graphical models UGM: Matlab code for undirected graphical models
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