*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.

*"synopsis" may belong to another edition of this title.*

"It assumes only a basic knowledge of probability, statistics andmathematics and is well suited for classroom teaching . . . Eachchapter of the book is concluded with short notes on the literatureand a set of helpful exercises." (Mathematical Reviews, 2011)

"Extensively tested in classroom teaching .The authorsclearly define all concepts and provide numerous examples andexercises." (*Book News*, December 2009)

*"About this title" may belong to another edition of this title.*

Published by
John Wiley and Sons Ltd, United States
(2009)

ISBN 10: 0470743042
ISBN 13: 9780470743041

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**Book Description **John Wiley and Sons Ltd, United States, 2009. Hardback. Book Condition: New. 1. Auflage. 250 x 170 mm. Language: English . Brand New 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. Bookseller Inventory # AAH9780470743041

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**Book Description **Somerset, New Jersey, U.S.A.: John Wiley & Sons Inc, 2009. Hardcover. Book Condition: New. Ship out 1-2 business day,Brand new,US edition, Free tracking number usually 2-4 biz days delivery to worldwide Same shipping fee with US, Canada,Europe country, Australia, item will ship out from either LA or Asia,t. Bookseller Inventory # ABE-7665892978

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**Book Description **Wileyand#8211;Blackwell, 2009. HRD. Book Condition: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Bookseller Inventory # FW-9780470743041

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Published by
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**Book Description **John Wiley and Sons Ltd, United States, 2009. Hardback. Book Condition: New. 1. Auflage. 250 x 170 mm. Language: English . Brand New 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. Bookseller Inventory # AAH9780470743041

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**Book Description **John Wiley and Sons Ltd. Hardback. Book Condition: new. BRAND NEW, Bayesian Networks: An Introduction, Timo Koski, John Noble, 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. Bookseller Inventory # B9780470743041

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**Book Description **John Wiley and Sons Ltd, 2009. Book Condition: New. Bayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks is a self-contained introduction to the theory and applications of Bayesian networks. Series: Wiley Series in Probability and Statistics. Num Pages: 366 pages, Illustrations. BIC Classification: PBT. Category: (U) Tertiary Education (US: College); (UU) Undergraduate. Dimension: 253 x 176 x 26. Weight in Grams: 788. . 2009. 1st Edition. Hardcover. . . . . . Bookseller Inventory # V9780470743041

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Published by
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**Book Description **John Wiley and Sons Ltd. Book Condition: New. Bayesian networks have found application in a number of fields, including risk analysis, consumer help desks, tissue pathology, pattern recognition, credit assessment, computer network diagnosis, and artificial intelligence. Bayesian Networks is a self-contained introduction to the theory and applications of Bayesian networks. Series: Wiley Series in Probability and Statistics. Num Pages: 366 pages, Illustrations. BIC Classification: PBT. Category: (U) Tertiary Education (US: College); (UU) Undergraduate. Dimension: 253 x 176 x 26. Weight in Grams: 788. . 2009. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland. Bookseller Inventory # V9780470743041

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