Advances in Probabilistic Graphical Models

  • Peter Lucas
  • José A. Gámez
  • Antonio Salmerón

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 214)

Table of contents

  1. Front Matter
    Pages I-X
  2. Foundations

    1. Front Matter
      Pages I-X
    2. Ildikó Flesch, Peter J.F. Lucas
      Pages 3-38
    3. James Q. Smith, Liliana J. Figueroa
      Pages 39-54
    4. Barry R. Cobb, Rafael Rumí, Antonio Salmerón
      Pages 81-102
    5. Linda C. van der Gaag, Silja Renooij, Veerle M.H. Coupé
      Pages 103-124
  3. Inference

    1. Front Matter
      Pages I-X
    2. Janneke H. Bolt, Linda C. van der Gaag
      Pages 153-173
  4. Learning

    1. Front Matter
      Pages I-X
    2. Jorge Muruzábal, Carlos Cotta
      Pages 193-213
  5. Decision Processes

    1. Front Matter
      Pages I-X
    2. Daniel Garcia-Sanchez, Marek J. Druzdzel
      Pages 255-273
    3. Søren Holbech Nielsen, Thomas D. Nielsen, Finn V. Jensen
      Pages 275-294
    4. L. Enrique Sucar
      Pages 295-309
  6. Applications

    1. Front Matter
      Pages I-X
    2. Anders L. Madsen, Uffe B. Kjærulff
      Pages 313-332
    3. Peter J.F. Lucas
      Pages 333-358
    4. Jose M. Peña, Johan Björkegren, Jesper Tegnér
      Pages 359-375
    5. Marcel van Gerven, Peter J.F. Lucas
      Pages 377-396

About this book


In recent years considerable progress has been made in the area of probabilistic graphical models, in particular Bayesian networks and influence diagrams. Probabilistic graphical models have become mainstream in the area of uncertainty in artificial intelligence;
contributions to the area are coming from computer science, mathematics, statistics and engineering.

This carefully edited book brings together in one volume some of the most important topics of current research in probabilistic graphical modelling, learning from data and probabilistic inference. This includes topics such as the characterisation of conditional
independence, the sensitivity of the underlying probability distribution of a Bayesian network to variation in its parameters, the learning of graphical models with latent variables and extensions to the influence diagram formalism.  In addition, attention is given to important application fields of probabilistic graphical models, such as the control of vehicles, bioinformatics and medicine.


Bayesian network Graph Markov Probability distribution Triangulation algorithms artificial intelligence autonom bioinformatics classification learning modelling probabilistic network statistics uncertainty

Editors and affiliations

  • Peter Lucas
    • 1
  • José A. Gámez
    • 2
  • Antonio Salmerón
    • 3
  1. 1.Institute for Computing and Information SciencesRadboud University NijmegenNijmegenThe Netherlands
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  3. 3.Department of Statistics and Applied MathematicsThe University of AlmeríaAlmeríaSpain

Bibliographic information

  • DOI
  • Copyright Information Springer 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-68994-2
  • Online ISBN 978-3-540-68996-6
  • Series Print ISSN 1434-9922
  • Series Online ISSN 1860-0808
  • Buy this book on publisher's site