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On the Optimality of Solutions of the MaxProduct Belief Propagation Algorithm in Arbitrary Graphs
, 2001
"... Graphical models, suchasBayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The maxproduct "belief propagation" algorithm is a localmessage passing algorithm on this graph that is known to converge to a unique fixed point when the gra ..."
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Cited by 241 (13 self)
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Graphical models, suchasBayesian networks and Markov random fields, represent statistical dependencies of variables by a graph. The maxproduct "belief propagation" algorithm is a localmessage passing algorithm on this graph that is known to converge to a unique fixed point when
Estimation and prediction for stochastic blockstructures.
 Journal of the American Statistical Association
, 2001
"... A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into several unobserved (latent) classes and that the probability distribution of the relation between two vertices depen ..."
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Cited by 231 (5 self)
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A statistical approach to a posteriori blockmodeling for digraphs and valued digraphs is proposed. The probability model assumes that the vertices of the digraph are partitioned into several unobserved (latent) classes and that the probability distribution of the relation between two vertices
tan
, 2011
"... Curcumin regulates prostaglandin (PG) synthesis in a variety of cells. PGE 2 and PGI 2 are generated from arachidonic acid (AA) by cyclooxygenases 1 and 2 (COX1 and COX2) and the synthase (PGES and PGI 2 S) pathways. This study evaluates the in vitro effect of curcumin on the expression of COX1, ..."
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treatment groups. Followup posthoc analyses were performed using the StudentNewmanKeuls procedure. To test for a synergy effect for the combined use of two agents, a cellmeans model approach to the synergy hypothesis was used, requiring the use of the IML procedure in SAS to derive test statistics from
Markov random fields with efficient approximations
 In IEEE Conference on Computer Vision and Pattern Recognition
, 1998
"... Markov Random Fields (MRF’s) can be used for a wide variety of vision problems. In this paper we focus on MRF’s with twovalued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut pro ..."
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Cited by 210 (23 self)
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Markov Random Fields (MRF’s) can be used for a wide variety of vision problems. In this paper we focus on MRF’s with twovalued clique potentials, which form a generalized Potts model. We show that the maximum a posteriori estimate of such an MRF can be obtained by solving a multiway minimum cut
LowComplexity Image Denoising Based on Statistical Modeling of Wavelet Coefficients
, 1999
"... We introduce a simple spatially adaptive statistical model for wavelet image coe#cients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the Estimation Quantization coder. We model wavelet image coefficients as zeromean Gaussian random varia ..."
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Cited by 189 (13 self)
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We introduce a simple spatially adaptive statistical model for wavelet image coe#cients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the Estimation Quantization coder. We model wavelet image coefficients as zeromean Gaussian random
Temporal sequence learning and data reduction for anomaly detection
 ACM TRANSACTIONS ON INFORMATION SYSTEMS SECURITY
, 1999
"... The anomaly detection problem can be formulated as one of learning to characterize the behaviors of an individual, system, or network in terms of temporal sequences of discrete data. We present an approach to this problem based on instance based learning (IBL) techniques. To cast the anomaly detecti ..."
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Cited by 191 (6 self)
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detection task in an IBL framework, we employ an approach that transforms temporal sequences of discrete, unordered observations into a metric space via a similarity measure that encodes intraattribute dependencies. Classification boundaries are selected from an a posteriori characterization of the valid
a Posteriori
 in Energy Minimization Methods in Computer Vision and Pattern
, 2001
"... Using first principles, we establish in this paper a connection between the maximum a posteriori (MAP) estimator and the variational fri ulationof optimizing a givenfH0O#k5zfl subject to some noise constraints. A MAP estimator which uses a Markov or a maximum entropy random field model fl a prior di ..."
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Using first principles, we establish in this paper a connection between the maximum a posteriori (MAP) estimator and the variational fri ulationof optimizing a givenfH0O#k5zfl subject to some noise constraints. A MAP estimator which uses a Markov or a maximum entropy random field model fl a prior
Bayesian color constancy
 Journal of the Optical Society of America A
, 1997
"... The problem of color constancy may be solved if we can recover the physical properties of illuminants and surfaces from photosensor responses. We consider this problem within the framework of Bayesian decision theory. First, we model the relation among illuminants, surfaces, and photosensor response ..."
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Cited by 188 (23 self)
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The problem of color constancy may be solved if we can recover the physical properties of illuminants and surfaces from photosensor responses. We consider this problem within the framework of Bayesian decision theory. First, we model the relation among illuminants, surfaces, and photosensor
TAN classifiers based on decomposable distributions
 Machine Learning
, 2005
"... Abstract. In this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN’s, we can compute the exact Bayesian model averaging ..."
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Cited by 3 (0 self)
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Abstract. In this paper we present several Bayesian algorithms for learning Tree Augmented Naive Bayes (TAN) models. We extend the results in Meila & Jaakkola (2000a) to TANs by proving that accepting a prior decomposable distribution over TAN’s, we can compute the exact Bayesian model
Videobased face recognition using probabilistic appearance manifolds
 In Proc. IEEE Conference on Computer Vision and Pattern Recognition
, 2003
"... This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a lowdimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds), ..."
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Cited by 176 (5 self)
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This paper presents a novel method to model and recognize human faces in video sequences. Each registered person is represented by a lowdimensional appearance manifold in the ambient image space. The complex nonlinear appearance manifold expressed as a collection of subsets (named pose manifolds
Results 11  20
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