logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Bayesian Networks An Introduction 1st Edition Timo Koski John Noble

  • SKU: BELL-4452860
Bayesian Networks An Introduction 1st Edition Timo Koski John Noble
$ 31.00 $ 45.00 (-31%)

0.0

0 reviews

Bayesian Networks An Introduction 1st Edition Timo Koski John Noble instant download after payment.

Publisher: Wiley
File Extension: PDF
File size: 1.86 MB
Pages: 366
Author: Timo Koski, John Noble
ISBN: 9780470743041, 0470743042
Language: English
Year: 2009
Edition: 1

Product desciption

Bayesian Networks An Introduction 1st Edition Timo Koski John Noble by Timo Koski, John Noble 9780470743041, 0470743042 instant download after payment.

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.

Related Products