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JMLR
2010
134views more  JMLR 2010»
14 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
FTML
2008
185views more  FTML 2008»
14 years 9 months ago
Graphical Models, Exponential Families, and Variational Inference
The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate stat...
Martin J. Wainwright, Michael I. Jordan
JMLR
2008
141views more  JMLR 2008»
14 years 9 months ago
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
In graphical modelling, a bi-directed graph encodes marginal independences among random variables that are identified with the vertices of the graph. We show how to transform a bi...
Mathias Drton, Thomas S. Richardson
CAINE
2008
14 years 11 months ago
A Graphics-User Interface in Support of a Cognitive Inference Architecture
- The objective of this paper is to present a graphical-user-interface (GUI) in support of a decision support system (KASER) for machine understanding. In order to provide informat...
Isai Michel Lombera, Jayeshkumar Patel, Stuart Har...
ECSQARU
2007
Springer
15 years 3 months ago
Causal Graphical Models with Latent Variables: Learning and Inference
Stijn Meganck, Philippe Leray, Bernard Manderick