Probabilistic graphical models

Probabilistic graphical models: principles and techniques

by Koller, Daphne

Type Book
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'Probabilistic Graphical Models' discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data.

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Title Statements

  • Full title :Probabilistic graphical models : principles and techniques / Daphne Koller, Nir Friedman.

Summary

'Probabilistic Graphical Models' discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data.

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Edition Statements

  • Publication Date:©2009

Publication Statements

  • Publisher(s):The MIT Press
  • Place of Publication:Cambridge, Massachusetts
  • Language:eng
  • Publication Date:©2009

The Physical Object

  • Description:xxxv, 1233 pages : : illustrations ; : 24 cm

Edition Identifiers

  • ISBN:0262013193, 9780262013192
  • oclc:ocn311310322
  • LC Classification:QA279.5
  • Dewey Decimal:519.5/420285

Table of Contents

1. Introduction -- 2. Foundations -- I. Representation -- 3. Bayesian Network Representation -- 4. Undirected Graphical Models -- 5. Local Probabilistic Models -- 6. Template-Based Representations -- 7. Gaussian Network Models -- 8. Exponential Family -- II. Inference -- 9. Exact Inference: Variable Elimination -- 10. Exact Inference: Clique Trees -- 11. Inference as Optimization -- 12. Particle-Based Approximate Inference -- 13. MAP Inference -- 14. Inference in Hybrid Networks -- 15. Inference in Temporal Models -- III. Learning -- 16. Learning Graphical Models: Overview -- 17. Parameter Estimation -- 18. Structure Learning in Bayesian Networks -- 19. Partially Observed Data -- 20. Learning Undirected Models -- IV. Actions and Decisions -- 21. Causality -- 22. Utilities and Decisions -- 23. Structured Decision Problems -- 24. Epilogue -- A. Background Material.

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