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» Statistics for complex random variables revisited
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APPROX
2010
Springer
213views Algorithms» more  APPROX 2010»
13 years 9 months ago
Constructive Proofs of Concentration Bounds
We give a simple combinatorial proof of the Chernoff-Hoeffding concentration bound [Che52, Hoe63], which says that the sum of independent {0, 1}-valued random variables is highly ...
Russell Impagliazzo, Valentine Kabanets
CVPR
2010
IEEE
13 years 11 months ago
Ray Markov Random Fields for Image-Based 3D Modeling: Model and Efficient Inference
In this paper, we present an approach to multi-view image-based 3D reconstruction by statistically inversing the ray-tracing based image generation process. The proposed algorithm...
Shubao Liu, David Cooper
INFOCOM
2011
IEEE
12 years 11 months ago
Modeling residual-geometric flow sampling
Abstract—Traffic monitoring and estimation of flow parameters in high speed routers have recently become challenging as the Internet grew in both scale and complexity. In this ...
Xiaoming Wang, Xiaoyong Li, Dmitri Loguinov
FTML
2008
185views more  FTML 2008»
13 years 7 months ago
Graphical Models, Exponential Families, and Variational Inference
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Martin J. Wainwright, Michael I. Jordan
CORR
2010
Springer
154views Education» more  CORR 2010»
13 years 7 months ago
Causal Markov condition for submodular information measures
The causal Markov condition (CMC) is a postulate that links observations to causality. It describes the conditional independences among the observations that are entailed by a cau...
Bastian Steudel, Dominik Janzing, Bernhard Sch&oum...