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UAI
2008
13 years 9 months ago
2008
Variational Bayesian inference and (collapsed) Gibbs sampling are the two important classes of inference algorithms for Bayesian networks. Both have their advantages and disadvant...
SYNTHESE
2011
13 years 2 months ago
2011
Scientific and statistical inferences build heavily on explicit, parametric models, and often with good reasons. However, the limited scope of parametric models and the increasin...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
ECML
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. In contrast to the standard inductive inference setting of predictive machine learning, in real world learning problems often the test instances are already available at ...