50 search results - page 2 / 10 » Inference of Graphical Causal Models: Representing the Meani... |
FTML
2008
13 years 6 months ago
2008
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
JMLR
2011
13 years 1 months ago
2011
We present a class of graphical models for directly representing the joint cumulative distribution function (CDF) of many random variables, called cumulative distribution networks...
AI
2008
Springer
13 years 4 months ago
2008
Springer
Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasonin...
NIPS
2004
13 years 7 months ago
2004
Directed graphical models with one layer of observed random variables and one or more layers of hidden random variables have been the dominant modelling paradigm in many research ...
CVPR
2003
IEEE
14 years 8 months ago
2003
IEEE
Probabilistic models have been adopted for many computer vision applications, however inference in highdimensional spaces remains problematic. As the statespace of a model grows, ...