Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Information Science and Statistics

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

    • 74,99 €
    • 74,99 €

Descrizione dell’editore

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new sections, in addition to fully-updated examples, tables, figures, and a revised appendix.  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 or her level of understanding. The techniques and methods presented on model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined based on numerous courses the authors have held for practitioners worldwide.  

Uffe B. Kjærulff holds a PhD on probabilistic networks and is an Associate Professor of Computer Science at Aalborg University. Anders L. Madsen of HUGIN EXPERT A/S holds a PhD on probabilistic networks and is an Adjunct Professor of Computer Science at Aalborg University.

GENERE
Computer e internet
PUBBLICATO
2012
30 novembre
LINGUA
EN
Inglese
PAGINE
400
EDITORE
Springer New York
DIMENSIONE
5,8
MB

Altri libri di Uffe B. Kjærulff & Anders L. Madsen

Altri libri di questa serie

Support Vector Machines Support Vector Machines
2008
Novelty, Information and Surprise Novelty, Information and Surprise
2023
Information and Complexity in Statistical Modeling Information and Complexity in Statistical Modeling
2007
Probabilistic Conditional Independence Structures Probabilistic Conditional Independence Structures
2006
Statistical and Inductive Inference by Minimum Message Length Statistical and Inductive Inference by Minimum Message Length
2005
Bayesian Networks and Decision Graphs Bayesian Networks and Decision Graphs
2009