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JMLR
2008
209views more  JMLR 2008»
13 years 10 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
JMLR
2011
145views more  JMLR 2011»
13 years 5 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
JMLR
2010
134views more  JMLR 2010»
13 years 4 months ago
Inference of Graphical Causal Models: Representing the Meaningful Information of Probability Distributions
This paper studies the feasibility and interpretation of learning the causal structure from observational data with the principles behind the Kolmogorov Minimal Sufficient Statist...
Jan Lemeire, Kris Steenhaut
ICDE
2011
IEEE
338views Database» more  ICDE 2011»
13 years 1 months ago
Outlier detection on uncertain data: Objects, instances, and inferences
—This paper studies the problem of outlier detection on uncertain data. We start with a comprehensive model considering both uncertain objects and their instances. An uncertain o...
Bin Jiang, Jian Pei
ICFP
2005
ACM
14 years 10 months ago
Type inference, principal typings, and let-polymorphism for first-class mixin modules
module is a programming abstraction that simultaneously generalizes -abstractions, records, and mutually recursive definitions. Although various mixin module type systems have bee...
Henning Makholm, J. B. Wells