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ICFP
2004
ACM
15 years 9 months ago
Types, potency, and idempotency: why nonlinearity and amnesia make a type system work
Useful type inference must be faster than normalization. Otherwise, you could check safety conditions by running the program. We analyze the relationship between bounds on normali...
Harry G. Mairson, Peter Møller Neergaard
PASTE
2010
ACM
15 years 3 months ago
Learning universal probabilistic models for fault localization
Recently there has been significant interest in employing probabilistic techniques for fault localization. Using dynamic dependence information for multiple passing runs, learnin...
Min Feng, Rajiv Gupta
IJCAI
2001
14 years 11 months ago
Approximate inference for first-order probabilistic languages
A new, general approach is described for approximate inference in first-order probabilistic languages, using Markov chain Monte Carlo (MCMC) techniques in the space of concrete po...
Hanna Pasula, Stuart J. Russell
JFP
2008
97views more  JFP 2008»
14 years 8 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
BMCBI
2008
98views more  BMCBI 2008»
14 years 10 months ago
Empirical Bayes models for multiple probe type microarrays at the probe level
Background: When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are...
Magnus Åstrand, Petter Mostad, Mats Rudemo