Bayesian Networks

An Introduction

Nonfiction, Science & Nature, Mathematics, Probability, Statistics
Cover of the book Bayesian Networks by Timo Koski, John Noble, Wiley
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Author: Timo Koski, John Noble ISBN: 9781119964957
Publisher: Wiley Publication: August 26, 2011
Imprint: Wiley Language: English
Author: Timo Koski, John Noble
ISBN: 9781119964957
Publisher: Wiley
Publication: August 26, 2011
Imprint: Wiley
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

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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