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» Approximation and limiting behavior of random models
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ICIP
2006
IEEE
14 years 9 months ago
Laplace Random Vectors, Gaussian Noise, and the Generalized Incomplete Gamma Function
Wavelet domain statistical modeling of images has focused on modeling the peaked heavy-tailed behavior of the marginal distribution and on modeling the dependencies between coeffi...
Ivan W. Selesnick
WSC
2001
13 years 8 months ago
Deterministic fluid models of congestion control in high-speed networks
Congestion control algorithms, such as TCP or the closelyrelated additive increase-multiplicative decrease algorithms, are extremely difficult to simulate on a large scale. The re...
Sanjay Shakkottai, R. Srikant
CORR
2010
Springer
116views Education» more  CORR 2010»
13 years 7 months ago
Estimation with Random Linear Mixing, Belief Propagation and Compressed Sensing
Abstract--We apply Guo and Wang's relaxed belief propagation (BP) method to the estimation of a random vector from linear measurements followed by a componentwise probabilisti...
Sundeep Rangan
CVPR
2009
IEEE
15 years 2 months ago
Global Connectivity Potentials for Random Field Models
Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions a...
Sebastian Nowozin, Christoph H. Lampert
CORR
2006
Springer
76views Education» more  CORR 2006»
13 years 7 months ago
Inconsistent parameter estimation in Markov random fields: Benefits in the computation-limited setting
Consider the problem of joint parameter estimation and prediction in a Markov random field: i.e., the model parameters are estimated on the basis of an initial set of data, and th...
Martin J. Wainwright