Bayesian Networks
eBook - ePub

Bayesian Networks

An Introduction

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Bayesian Networks

An Introduction

About this book

Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout.

Features include:

  • An introduction to Dirichlet Distribution, Exponential Families and their applications.
  • A detailed description of learning algorithms and Conditional Gaussian Distributions using Junction Tree methods.
  • A discussion of Pearl's intervention calculus, with an introduction to the notion of see and do conditioning.
  • All concepts are clearly defined and illustrated with examples and exercises. Solutions are provided online.

This book will prove a valuable resource for postgraduate students of statistics, computer engineering, mathematics, data mining, artificial intelligence, and biology.

Researchers and users of comparable modelling or statistical techniques such as neural networks will also find this book of interest.

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Yes, you can access Bayesian Networks by Timo Koski,John Noble in PDF and/or ePUB format, as well as other popular books in Mathématiques & Probabilités et statistiques. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2011
Print ISBN
9780470743041
eBook ISBN
9781119964957

Table of contents

  1. Cover
  2. Series Page
  3. Title Page
  4. Copyright
  5. Preface
  6. 1 Graphical models and probabilistic reasoning
  7. 2 Conditional independence, graphs and d -separation
  8. 3 Evidence, sufficiency and Monte Carlo methods
  9. 4 Decomposable graphs and chain graphs
  10. 5 Learning the conditional probability potentials
  11. 6 Learning the graph structure
  12. 7 Parameters and sensitivity
  13. 8 Graphical models and exponential families
  14. 9 Causality and intervention calculus
  15. 10 The junction tree and probability updating
  16. 11 Factor graphs and the sum product algorithm
  17. References
  18. Index