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» Exploiting Causal Independence in Large Bayesian Networks
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JMLR
2010
134views more  JMLR 2010»
13 years 2 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
CCS
2008
ACM
13 years 9 months ago
Measuring network security using dynamic bayesian network
Given the increasing dependence of our societies on networked information systems, the overall security of these systems should be measured and improved. Existing security metrics...
Marcel Frigault, Lingyu Wang, Anoop Singhal, Sushi...
FLAIRS
2009
13 years 5 months ago
Constraint-based Approach to Discovery of Inter Module Dependencies in Modular Bayesian Networks
This paper introduces an information theoretic approach to verification of modular causal probabilistic models. We assume systems which are gradually extended by adding new functi...
Patrick de Oude, Gregor Pavlin
ECAI
2010
Springer
13 years 8 months ago
Context-Specific Independence in Directed Relational Probabilistic Models and its Influence on the Efficiency of Gibbs Sampling
Abstract. There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as ...
Daan Fierens
ICANN
2010
Springer
13 years 8 months ago
Discovery of Exogenous Variables in Data with More Variables Than Observations
Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations. However, modern datasets including gene expres...
Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärin...