Buch, Englisch, 447 Seiten, Format (B × H): 162 mm x 244 mm, Gewicht: 1830 g
Buch, Englisch, 447 Seiten, Format (B × H): 162 mm x 244 mm, Gewicht: 1830 g
Reihe: Information Science and Statistics
ISBN: 978-0-387-68281-5
Verlag: Springer
This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, also introduces Markov decision process. The new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Fuzzy-Systeme
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Naturwissenschaften Biowissenschaften Botanik Pflanzenreproduktion, Verbreitung, Genetik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
Weitere Infos & Material
Prerequisites on Probability Theory.- Prerequisites on Probability Theory.- Probabilistic Graphical Models.- Causal and Bayesian Networks.- Building Models.- Belief Updating in Bayesian Networks.- Analysis Tools for Bayesian Networks.- Parameter estimation.- Learning the Structure of Bayesian Networks.- Bayesian Networks as Classifiers.- Decision Graphs.- Graphical Languages for Specification of Decision Problems.- Solution Methods for Decision Graphs.- Methods for Analyzing Decision Problems.
