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JMLR
2002
74views more  JMLR 2002»
13 years 10 months ago
The Representational Power of Discrete Bayesian Networks
One of the most important fundamental properties of Bayesian networks is the representational power, reflecting what kind of functions they can or cannot represent. In this paper,...
Charles X. Ling, Huajie Zhang
FLAIRS
2003
14 years 8 days ago
An Extension of the Differential Approach for Bayesian Network Inference to Dynamic Bayesian Networks
We extend the differential approach to inference in Bayesian networks (BNs) (Darwiche, 2000) to handle specific problems that arise in the context of dynamic Bayesian networks (D...
Boris Brandherm
ISMIS
2005
Springer
14 years 4 months ago
Robust Inference of Bayesian Networks Using Speciated Evolution and Ensemble
Recently, there are many researchers to design Bayesian network structures using evolutionary algorithms but most of them use the only one fittest solution in the last generation. ...
Kyung-Joong Kim, Ji-Oh Yoo, Sung-Bae Cho
DASFAA
2011
IEEE
270views Database» more  DASFAA 2011»
13 years 2 months ago
AutoBayesian: Developing Bayesian Networks Based on Text Mining
Bayesian network is a widely used tool for data analysis, modeling and decision support in various domains. There is a growing need for techniques and tools which can automatically...
Sandeep Raghuram, Yuni Xia, Jiaqi Ge, Mathew Palak...
AI
2006
Springer
13 years 11 months ago
Controlled generation of hard and easy Bayesian networks: Impact on maximal clique size in tree clustering
This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. ...
Ole J. Mengshoel, David C. Wilkins, Dan Roth