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Advanced Methodologies for Bayesian Networks: Lecture Notes in Computer Science, cartea 9505

Editat de Joe Suzuki, Maomi Ueno
en Limba Engleză Paperback – 25 feb 2016
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.

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Specificații

ISBN-13: 9783319283784
ISBN-10: 3319283782
Pagini: 284
Ilustrații: XVIII, 265 p. 102 illus. in color.
Dimensiuni: 155 x 235 x 16 mm
Greutate: 0.44 kg
Ediția:1st edition 2015
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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

Textul de pe ultima copertă

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.

Caracteristici

Includes supplementary material: sn.pub/extras