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Graphical models, exponential families, and variational inference
, 2008
"... The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building largescale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fiel ..."
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Cited by 819 (28 self)
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likelihoods, marginal probabilities and most probable configurations. We describe how a wide varietyof algorithms — among them sumproduct, cluster variational methods, expectationpropagation, mean field methods, maxproduct and linear programming relaxation, as well as conic programming relaxations — can
A subexponential lower bound for the Random Facet algorithm for Parity Games
"... Parity Games form an intriguing family of infinite duration games whose solution is equivalent to the solution of important problems in automatic verification and automata theory. They also form a very natural subclass of Deterministic Mean Payoff Games, which in turn is a very natural subclass of t ..."
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Cited by 6 (5 self)
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of turnbased Stochastic Mean Payoff Games. It is a major open problem whether these game families can be solved in polynomial time. The currently fastest algorithms for the solution of all these games are adaptations of the randomized generalizationof linear programming. We refer to the algorithm of
Experimental Analysis of Neighborhood Effects on Youth,” Working Paper 483, Industrial Relations Section,
, 2004
"... Families, primarily femaleheaded minority households with children, living in highpoverty public housing projects in five U.S. cities were offered housing vouchers by lottery in the Moving to Opportunity program. Four to seven years after random assignment, families offered vouchers lived in safer ..."
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Cited by 323 (18 self)
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Families, primarily femaleheaded minority households with children, living in highpoverty public housing projects in five U.S. cities were offered housing vouchers by lottery in the Moving to Opportunity program. Four to seven years after random assignment, families offered vouchers lived
A Subexponential Bound for Linear Programming
 ALGORITHMICA
, 1996
"... We present a simple randomized algorithm which solves linear programs with n constraints and d variables in expected min{O(d 2 2 d n),e 2 d ln(n / d)+O ( d+ln n)} time in the unit cost model (where we count the number of arithmetic operations on the numbers in the input); to be precise, the algorith ..."
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Cited by 185 (15 self)
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We present a simple randomized algorithm which solves linear programs with n constraints and d variables in expected min{O(d 2 2 d n),e 2 d ln(n / d)+O ( d+ln n)} time in the unit cost model (where we count the number of arithmetic operations on the numbers in the input); to be precise
On sparse reconstruction from Fourier and Gaussian measurements
 Communications on Pure and Applied Mathematics
, 2006
"... Abstract. This paper improves upon best known guarantees for exact reconstruction of a sparse signal f from a small universal sample of Fourier measurements. The method for reconstruction that has recently gained momentum in the Sparse Approximation Theory is to relax this highly nonconvex problem ..."
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Cited by 262 (8 self)
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to a convex problem, and then solve it as a linear program. We show that there exists a set of frequencies Ω such that one can exactly reconstruct every rsparse signal f of length n from its frequencies in Ω, using the convex relaxation, and Ω has size k(r, n) = O(r log(n)·log 2 (r) log(r log n)) = O
MAP estimation via agreement on trees: Messagepassing and linear programming
, 2002
"... We develop and analyze methods for computing provably optimal maximum a posteriori (MAP) configurations for a subclass of Markov random fields defined on graphs with cycles. By decomposing the original distribution into a convex combination of treestructured distributions, we obtain an upper bound ..."
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Cited by 191 (9 self)
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is that any such shared configuration must also be a MAP configuration for the original distribution. Next we develop two approaches to attempting to obtain tight upper bounds: (a) a treerelaxed linear program (LP), which is derived from the Lagrangian dual of the upper bounds; and (b) a treereweighted max
Application of hierarchical linear models to assessing change.
 Psychological Bulletin,
, 1987
"... Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A twostage model of change is proposed here. At the first, or withinsubject stage, an individual's s ..."
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Cited by 207 (5 self)
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Recent advances in the statistical theory of hierarchical linear models should enable important breakthroughs in the measurement of psychological change and the study of correlates of change. A twostage model of change is proposed here. At the first, or withinsubject stage, an individual
Approximate Graph Coloring by Semidefinite Programming.
 In Proceedings of 35th Annual IEEE Symposium on Foundations of Computer Science,
, 1994
"... Abstract. We consider the problem of coloring kcolorable graphs with the fewest possible colors. We present a randomized polynomial time algorithm that colors a 3colorable graph on n vertices with min{O(⌬ 1/3 log 1/2 ⌬ log n), O(n 1/4 log 1/2 n)} colors where ⌬ is the maximum degree of any vertex ..."
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Cited by 210 (7 self)
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)} colors. Our results are inspired by the recent work of Goemans and Williamson who used an algorithm for semidefinite optimization problems, which generalize linear programs, to obtain improved approximations for the MAX CUT and MAX 2SAT problems. An intriguing outcome of our work is a duality
Smallest Enclosing Disks (balls and Ellipsoids)
 Results and New Trends in Computer Science
, 1991
"... A simple randomized algorithm is developed which computes the smallest enclosing disk of a finite set of points in the plane in expected linear time. The algorithm is based on Seidel's recent Linear Programming algorithm, and it can be generalized to computing smallest enclosing balls or ellips ..."
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Cited by 209 (5 self)
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A simple randomized algorithm is developed which computes the smallest enclosing disk of a finite set of points in the plane in expected linear time. The algorithm is based on Seidel's recent Linear Programming algorithm, and it can be generalized to computing smallest enclosing balls
An algorithmic introduction to numerical simulation of stochastic differential
 equations,” SIAM Review,
, 2001
"... Abstract. A practical and accessible introduction to numerical methods for stochastic differential equations is given. The reader is assumed to be familiar with Euler's method for deterministic differential equations and to have at least an intuitive feel for the concept of a random variable; ..."
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Cited by 207 (2 self)
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; however, no knowledge of advanced probability theory or stochastic processes is assumed. The article is built around 10 MATLAB programs, and the topics covered include stochastic integration, the EulerMaruyama method, Milstein's method, strong and weak convergence, linear stability
Results 1  10
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