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UAI
2003
13 years 9 months ago
Large-Sample Learning of Bayesian Networks is NP-Hard
In this paper, we provide new complexity results for algorithms that learn discrete-variable Bayesian networks from data. Our results apply whenever the learning algorithm uses a ...
David Maxwell Chickering, Christopher Meek, David ...
UAI
2003
13 years 9 months ago
Robust Independence Testing for Constraint-Based Learning of Causal Structure
This paper considers a method that combines ideas from Bayesian learning, Bayesian network inference, and classical hypothesis testing to produce a more reliable and robust test o...
Denver Dash, Marek J. Druzdzel
AIME
2007
Springer
13 years 11 months ago
Using Temporal Context-Specific Independence Information in the Exploratory Analysis of Disease Processes
Abstract. Disease processes in patients are temporal in nature and involve uncertainty. It is necessary to gain insight into these processes when aiming at improving the diagnosis,...
Stefan Visscher, Peter J. F. Lucas, Ildikó ...
AI
2002
Springer
13 years 7 months ago
Learning Bayesian networks from data: An information-theory based approach
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
ICDM
2002
IEEE
143views Data Mining» more  ICDM 2002»
14 years 17 days ago
A Hybrid Approach to Discover Bayesian Networks From Databases Using Evolutionary Programming
This paper describes a novel data mining approach that employs evolutionary programming to discover knowledge represented in Bayesian networks. There are two different approaches ...
Man Leung Wong, Shing Yan Lee, Kwong-Sak Leung