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» Racing for Conditional Independence Inference
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JMLR
2011
145views more  JMLR 2011»
14 years 4 months ago
Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Func
We present a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variables, called cumulative distribution networks...
Jim C. Huang, Brendan J. Frey
AAAI
1996
14 years 11 months ago
Irrelevance and Conditioning in First-Order Probabilistic Logic
First-order probabilistic logic is a powerful knowledge representation language. Unfortunately, deductive reasoning based on the standard semantics for this logic does not support...
Daphne Koller, Joseph Y. Halpern
ICML
2010
IEEE
14 years 10 months ago
Exploiting Data-Independence for Fast Belief-Propagation
Maximum a posteriori (MAP) inference in graphical models requires that we maximize the sum of two terms: a data-dependent term, encoding the conditional likelihood of a certain la...
Julian John McAuley, Tibério S. Caetano
BMCBI
2007
129views more  BMCBI 2007»
14 years 9 months ago
Inferring cellular networks - a review
In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show t...
Florian Markowetz, Rainer Spang
SDM
2008
SIAM
138views Data Mining» more  SDM 2008»
14 years 11 months ago
Learning Markov Network Structure using Few Independence Tests
In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for...
Parichey Gandhi, Facundo Bromberg, Dimitris Margar...