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» The Complexity of Distinguishing Markov Random Fields
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APPROX
2008
Springer
119views Algorithms» more  APPROX 2008»
14 years 11 months ago
The Complexity of Distinguishing Markov Random Fields
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
Andrej Bogdanov, Elchanan Mossel, Salil P. Vadhan
ICPR
2002
IEEE
15 years 10 months ago
Text Segmentation and Recognition in Complex Background Based on Markov Random Field
Datong Chen, Jean-Marc Odobez, Hervé Bourla...
BMCBI
2007
160views more  BMCBI 2007»
14 years 9 months ago
Identifying protein complexes directly from high-throughput TAP data with Markov random fields
Background: Predicting protein complexes from experimental data remains a challenge due to limited resolution and stochastic errors of high-throughput methods. Current algorithms ...
Wasinee Rungsarityotin, Roland Krause, Arno Sch&ou...
TSP
2010
14 years 4 months ago
Randomized and distributed self-configuration of wireless networks: two-layer Markov random fields and near-optimality
Abstract--This work studies the near-optimality versus the complexity of distributed configuration management for wireless networks. We first develop a global probabilistic graphic...
Sung-eok Jeon, Chuanyi Ji
ICASSP
2011
IEEE
14 years 1 months ago
A new stochastic image model based on Markov random fields and its application to texture modeling
Stochastic image modeling based on conventional Markov random fields is extensively discussed in the literature. A new stochastic image model based on Markov random fields is intr...
Siamak Yousefi, Nasser D. Kehtarnavaz