53 search results - page 7 / 11 » Expectation Propagation for approximate Bayesian inference |
AI
2010
Springer
13 years 7 months ago
2010
Springer
Bayesian networks (BNs) are used to represent and ef ciently compute with multi-variate probability distributions in a wide range of disciplines. One of the main approaches to per...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
IJAR
2008
13 years 7 months ago
2008
This paper presents a family of algorithms for approximate inference in credal networks (that is, models based on directed acyclic graphs and set-valued probabilities) that contai...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
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...