Marginals-to-Models Reducibility Michael Kearns University of Pennsylvania

First Page		Document Content
Date: 2013-11-08 17:46:03 Mathematics Computational complexity theory Graph theory Graphical models Combinatorial optimization Convex optimization Operations research Probability theory Polynomial Ellipsoid method Markov random field Bayesian network		Marginals-to-Models Reducibility Michael Kearns University of Pennsylvania Add to Reading List Source URL: theory.stanford.edu Download Document from Source Website File Size: 344,59 KB Share Document on Facebook

	Constraints DOIs10601y Graphical models for optimal power flow Krishnamurthy Dvijotham1 · Michael Chertkov2 · Pascal Van Hentenryck3 · Marc Vuffray2 · DocID: 1vlyo - View Document
	IFT 6269: Probabilistic Graphical Models Fall 2017 Lecture 3 — September 12 Lecturer: Simon Lacoste-Julien DocID: 1vkUj - View Document
	OR/STAT 719 – CSI 775 Graphical Probability Models for Inference and Decision Making Prof. Paulo C. G. Costa, PhD Department of Systems Engineering and Operations Research George Mason University DocID: 1uNvh - View Document
	Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models Baback Moghaddam1, Benjamin M. Marlin2, Mohammad Emtiyaz Khan2 and Kevin P. Murphy2 1. Jet Propulsion Laboratory, California Insti DocID: 1urTH - View Document