The authors would like to thank the editor and two anonymous reviewers and for their valuable feedback. This work is supported by the European Research Council (ERC-2013-AdG339182-BAYES-KNOWLEDGE) and the China Scholarship Council (CSC)/Queen Mary Joint PhD scholarships. YZ and CZ are supported by and National Natural Science Foundation of China (61273322, 71471174)
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Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...
Graduation date: 2005Machine learning encompasses probabilistic and statistical techniques that can ...
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AbstractWe consider the problem of learning the parameters of a Bayesian network from data, while ta...
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Abstract. Lack of relevant data is a major challenge for learning Bayesi-an networks (BNs) in real-w...
Learning parameters of a probabilistic model is a necessary step in most machine learning modeling t...
One of the essential problems on Bayesian networks (BNs) is parameter learning. When purely data-dri...
AbstractIn many realistic problem domains, the main variable of interest behaves monotonically in th...
International audienceExploiting experts' knowledge can significantly increase the quality of the Ba...
The task of learning models for many real-world problems requires incorporating domain knowledge in...
Domain experts can often quite reliably specify the sign of influences between variables in a Bayesi...
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially ...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...
Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...
Graduation date: 2005Machine learning encompasses probabilistic and statistical techniques that can ...
PhDOne of the hardest challenges in building a realistic Bayesian network (BN) model is to construc...
AbstractWe consider the problem of learning the parameters of a Bayesian network from data, while ta...
Abstract. In Bayesian networks, prior knowledge has been used in the form of causal independencies b...
Abstract. Lack of relevant data is a major challenge for learning Bayesi-an networks (BNs) in real-w...
Learning parameters of a probabilistic model is a necessary step in most machine learning modeling t...
One of the essential problems on Bayesian networks (BNs) is parameter learning. When purely data-dri...
AbstractIn many realistic problem domains, the main variable of interest behaves monotonically in th...
International audienceExploiting experts' knowledge can significantly increase the quality of the Ba...
The task of learning models for many real-world problems requires incorporating domain knowledge in...
Domain experts can often quite reliably specify the sign of influences between variables in a Bayesi...
Learning accurate Bayesian networks (BNs) is a key challenge in real-world applications, especially ...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...
Most documented Bayesian network (BN) applications have been built through knowledge elicitation fro...
A Bayesian network is graphical representation of the probabilistic relationships among set of varia...