Advanced Methodologies for Bayesian Networks
Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings
Herausgegeben: Suzuki, Joe; Ueno, Maomi
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This volume constitutes the refereed proceedings of theSecond International Workshop on Advanced Methodologies for Bayesian Networks,AMBN 2015, held in Yokohama, Japan, in November 2015.The 18 revised full papers and 6 invited abstractspresented were carefully reviewed and selected from numerous submissions. Inthe International Workshop on Advanced Methodologies for Bayesian Networks(AMBN), the researchers explore methodologies for enhancing the effectivenessof graphical models including modeling, reasoning, model selection,logic-probability relations, and causality. The exploration of methodo...
This volume constitutes the refereed proceedings of theSecond International Workshop on Advanced Methodologies for Bayesian Networks,AMBN 2015, held in Yokohama, Japan, in November 2015.
The 18 revised full papers and 6 invited abstractspresented were carefully reviewed and selected from numerous submissions. Inthe International Workshop on Advanced Methodologies for Bayesian Networks(AMBN), the researchers explore methodologies for enhancing the effectivenessof graphical models including modeling, reasoning, model selection,logic-probability relations, and causality. The exploration of methodologies iscomplemented discussions of practical considerations for applying graphicalmodels in real world settings, covering concerns like scalability, incrementallearning, parallelization, and so on.
The 18 revised full papers and 6 invited abstractspresented were carefully reviewed and selected from numerous submissions. Inthe International Workshop on Advanced Methodologies for Bayesian Networks(AMBN), the researchers explore methodologies for enhancing the effectivenessof graphical models including modeling, reasoning, model selection,logic-probability relations, and causality. The exploration of methodologies iscomplemented discussions of practical considerations for applying graphicalmodels in real world settings, covering concerns like scalability, incrementallearning, parallelization, and so on.
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