922 search results - page 19 / 185 » Learning Gaussian Process Models from Uncertain Data |
JCST
2010
13 years 5 months ago
2010
In the Bayesian mixture modeling framework it is possible to infer the necessary number of components to model the data and therefore it is unnecessary to explicitly restrict the n...
NIPS
1998
13 years 8 months ago
1998
A directed generative model for binary data using a small number of hidden continuous units is investigated. A clipping nonlinearity distinguishes the model from conventional prin...
ICA
2010
Springer
13 years 8 months ago
2010
Springer
Abstract. We discuss causal structure learning based on linear structural equation models. Conventional learning methods most often assume Gaussianity and create many indistinguish...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
EVOW
2008
Springer
13 years 9 months ago
2008
Springer
In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false...