Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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51e6d6e679953c6311757004d8cbbba9-Paper.pdf
Neural Information Processing Systems
Methods for inference and simulation of linearly constrained Gaussian Markov Random Fields (GMRF) are computationally prohibitive when the number of constraintsislarge.
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Necessary and Sufficient Conditions for Optimal Decision Trees using Dynamic Programming
Neural Information Processing Systems
Dynamic programming methods have been shown to scale much better because they exploit the tree structure by solving subtrees as independent subproblems.
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