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Advanced Methodologies for Bayesian Networks: Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings 1st ed. 2015 [Paperback / softback]

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  • Format: Paperback / softback, 265 pages, height x width: 235x155 mm, weight: 4336 g, 102 Illustrations, color; XVIII, 265 p. 102 illus. in color., 1 Paperback / softback
  • Series: Lecture Notes in Computer Science 9505
  • Pub. Date: 25-Feb-2016
  • Publisher: Springer International Publishing AG
  • ISBN-10: 3319283782
  • ISBN-13: 9783319283784
  • Paperback / softback
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  • Format: Paperback / softback, 265 pages, height x width: 235x155 mm, weight: 4336 g, 102 Illustrations, color; XVIII, 265 p. 102 illus. in color., 1 Paperback / softback
  • Series: Lecture Notes in Computer Science 9505
  • Pub. Date: 25-Feb-2016
  • Publisher: Springer International Publishing AG
  • ISBN-10: 3319283782
  • ISBN-13: 9783319283784
This volume constitutes the refereed proceedings of the Second International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, held in November 2015. The 18 revised full papers and 6 invited abstracts presented were carefully reviewed and selected from numerous submissions. In the International Workshop on Advanced Methodologies for Bayesian Networks (AMBN), the researchers explore methodologies for enhancing the effectiveness of graphical models including modeling, reasoning, model selection, logic-probability relations, and causality. The exploration of methodologies is complemented discussions of practical considerations for applying graphical models in real world settings, covering concerns like scalability, incremental learning, parallelization, and so on.

Effectivenessof graphical models including modeling. Reasoning, model selection.- Logic-probabilityrelations.- Causality. Applying graphical models in real world settings.- Scalability.- Incremental learning.-Parallelization.
Effectivenessof graphical models including modeling. Reasoning, model selection.- Logic-probabilityrelations.- Causality. Applying graphical models in real world settings.- Scalability.- Incremental learning.-Parallelization.