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Advances in Probabilistic Graphical Models|Peter Lucas

Advances in Probabilistic Graphical Models

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Overview

This book brings together important topics of current research in probabilistic graphical modeling, learning from data and probabilistic inference. Coverage includes such topics as the characterization of conditional independence, the learning of graphical models with latent variables, and extensions to the influence diagram formalism as well as important application fields, such as the control of vehicles, bioinformatics and medicine.

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Details

  • ISBN-13: 9783540689942
  • ISBN-10: 354068994X
  • Publisher: Springer
  • Publish Date: February 2007
  • Dimensions: 9.21 x 6.14 x 0.88 inches
  • Shipping Weight: 1.61 pounds
  • Page Count: 386

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