Advanced Methodologies for Bayesian Networks

Second International Workshop, AMBN 2015, Yokohama, Japan, November 16-18, 2015. Proceedings

  • Joe Suzuki
  • Maomi Ueno
Conference proceedings AMBN 2015

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9505)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 9505)

Table of contents

  1. Front Matter
    Pages I-XVIII
  2. Kazuki Natori, Masaki Uto, Yu Nishiyama, Shuichi Kawano, Maomi Ueno
    Pages 15-31
  3. Niklas Jahnsson, Brandon Malone, Petri Myllymäki
    Pages 46-60
  4. Alessio Benavoli, Cassio P. de Campos
    Pages 76-92
  5. Yun Zhou, John Howroyd, Sebastian Danicic, J. Mark Bishop
    Pages 93-104
  6. Karthika Mohan, Judea Pearl
    Pages 184-195
  7. Patrick Blöbaum, Shohei Shimizu, Takashi Washio
    Pages 209-221
  8. Back Matter
    Pages 265-265

About these proceedings


This volume constitutes the refereed proceedings of the Second 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 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.


Causal inference Convex optimization Genetic algorithm Linear programming Naive bayes Algorithm selection Bayesian networks Classification consistency Clustering Consistency Data imputation Empirical hardness Gene differential analysis Logistic regression Multi-linear functions Mutual information Parameter learning RFID Data Robust inference Structure learning

Editors and affiliations

  • Joe Suzuki
    • 1
  • Maomi Ueno
    • 2
  1. 1.Osaka UniversityOsakaJapan
  2. 2.The University of Electro-CommunicationsTokyoJapan

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-28378-4
  • Online ISBN 978-3-319-28379-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site
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