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IJAR
2010
75views more  IJAR 2010»
13 years 5 months ago
Generalized loopy 2U: A new algorithm for approximate inference in credal networks
Credal nets generalize Bayesian nets by relaxing the requirement of precision of probabilities. Credal nets are considerably more expressive than Bayesian nets, but this makes bel...
Alessandro Antonucci, Yi Sun, Cassio P. de Campos,...

Book
519views
15 years 6 months ago
Information Theory, Inference, and Learning Algorithms
This book is aimed at senior undergraduates and graduate students in Engineering, Science, Mathematics, and Computing. It expects familiarity with calculus, probability theory, and...
David J. C. MacKay
BMCBI
2010
172views more  BMCBI 2010»
13 years 7 months ago
Comparison of evolutionary algorithms in gene regulatory network model inference
Background: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineeri...
Alina Sîrbu, Heather J. Ruskin, Martin Crane
IJAR
2007
69views more  IJAR 2007»
13 years 7 months ago
Racing algorithms for conditional independence inference
In this article, we consider the computational aspects of deciding whether a conditional independence statement t is implied by a list of conditional independence statements L usi...
Remco R. Bouckaert, Milan Studený
JFP
2008
97views more  JFP 2008»
13 years 6 months ago
HM(X) type inference is CLP(X) solving
The HM(X) system is a generalization of the Hindley/Milner system parameterized in the constraint domain X. Type inference is performed by generating constraints out of the progra...
Martin Sulzmann, Peter J. Stuckey