Probabilistic Graphical Models

7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings

  • Linda C. van der Gaag
  • Ad J. Feelders
Conference proceedings PGM 2014

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8754)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 8754)

Table of contents

  1. Front Matter
  2. David Albrecht, Ann E. Nicholson, Chris Whittle
    Pages 1-16
  3. Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José M. Puerta
    Pages 17-32
  4. Ali Ben Mrad, Véronique Delcroix, Sylvain Piechowiak, Philip Leicester
    Pages 33-48
  5. Marcus Bendtsen, Jose M. Peña
    Pages 49-64
  6. Cory J. Butz, Jhonatan de S. Oliveira, Anders L. Madsen
    Pages 81-96
  7. Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen
    Pages 97-112
  8. Andrés Cano, Manuel Gómez-Olmedo, Serafín Moral, Cora B. Pérez-Ariza
    Pages 113-128
  9. Giorgio Corani, Alessandro Antonucci, Denis D. Mauá, Sandra Gabaglio
    Pages 145-159
  10. Cedric De Boom, Jasper De Bock, Arthur Van Camp, Gert de Cooman
    Pages 160-175
  11. Cassio P. de Campos, Marco Cuccu, Giorgio Corani, Marco Zaffalon
    Pages 176-189
  12. Antonio Fernández, Rafael Rumí, José del Sagrado, Antonio Salmerón
    Pages 206-221
  13. Anders L. Madsen, Frank Jensen, Martin Karlsen, Nicolaj Soendberg-Jeppesen
    Pages 286-301

About these proceedings

Introduction

This book constitutes the refereed proceedings of the 7th International Workshop on Probabilistic Graphical Models, PGM 2014, held in Utrecht, The Netherlands, in September 2014. The 38 revised full papers presented in this book were carefully reviewed and selected from 44 submissions. The papers cover all aspects of graphical models for probabilistic reasoning, decision making, and learning.

Keywords

Bayesian networks artificial intelligence belief networks classification data mining decision networks graph algorithms graph theory influence diagrams learning in probabilistic graphical models machine learning probabilistic representations probability and statistics search methods trees

Editors and affiliations

  • Linda C. van der Gaag
    • 1
  • Ad J. Feelders
    • 2
  1. 1.Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC UtrechtThe Netherlands
  2. 2.Utrecht University, Faculty of Science, Department of Information and Computing Sciences, Princetonplein 5, 3584 CC Utrecht,The Netherlands

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-11433-0
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-11432-3
  • Online ISBN 978-3-319-11433-0
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book