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Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis [Paperback]

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  • Category: Books (Computers)
  • Author:  Kj?rulff, Uffe B., Madsen, Anders L.
  • Author:  Kj?rulff, Uffe B., Madsen, Anders L.
  • ISBN-10:  1441925465
  • ISBN-10:  1441925465
  • ISBN-13:  9781441925466
  • ISBN-13:  9781441925466
  • Publisher:  Springer
  • Publisher:  Springer
  • Pages:  336
  • Pages:  336
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Jan-2010
  • Pub Date:  01-Jan-2010
  • SKU:  1441925465-11-SPRI
  • SKU:  1441925465-11-SPRI
  • Item ID: 100725129
  • List Price: $109.00
  • Seller: ShopSpell
  • Ships in: 5 business days
  • Transit time: Up to 5 business days
  • Delivery by: Jul 05 to Jul 07
  • Notes: Brand New Book. Order Now.

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence. This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.

This book provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. The theory and methods presented are illustrated through more than 140 examples.

Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty.

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding.

The techniques and methods presented for l£6

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