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» Explaining inferences in Bayesian networks
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UAI
1994
13 years 10 months ago
Approximation Algorithms for the Loop Cutset Problem
We show how to nd a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the rst step in the method of conditioning for inference....
Ann Becker, Dan Geiger
SYNTHESE
2008
130views more  SYNTHESE 2008»
13 years 8 months ago
Appropriateness measures: an uncertainty model for vague concepts
Abstract We argue that in the decision making process required for selecting assertible vague descriptions of an object, it is practical that communicating agents adopt an epistemi...
Jonathan Lawry
IJUFKS
2000
111views more  IJUFKS 2000»
13 years 8 months ago
A Factorized Representation of Independence of Causal Influence and Lazy Propagation
Theefficiency of algorithmsfor probabilistic inference in Bayesian networks can be improvedby exploiting independenceof causal influence. Thefactorized representation of independe...
Anders L. Madsen, Bruce D'Ambrosio
CVPR
2004
IEEE
14 years 10 months ago
Representation and Matching of Articulated Shapes
We consider the problem of localizing the articulated and deformable shape of a walking person in a single view. We represent the non-rigid 2D body contour by a Bayesian graphical...
Jiayong Zhang, Robert T. Collins, Yanxi Liu
JMM2
2007
221views more  JMM2 2007»
13 years 8 months ago
Automatic Extraction of Femur Contours from Calibrated X-Ray Images using Statistical Information
— Automatic identification and extraction of bone contours from x-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D s...
Xiao Dong, Miguel Ángel González Bal...