
Probabilistic Networks and Expert Systems
Exact Computational Methods for Bayesian Networks
Springer (Publisher)
1st Edition
Published on 16. July 2007
Book
Paperback/Softback
XII, 324 pages
978-0-387-71823-1 (ISBN)
Description
The work reviewed in this book represents the synthesis of two
important developments in modelling of complex stochastic phenomena.
This book will be an essential reference for people interested in
artificial intelligence in both computer science and statistics.
Reviews / Votes
From the reviews:
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
"This important book fills a void in the graphical Markov models literature. The authors have summarized their extensive and influential work in this area and provided a valuable resource both for educators and for practitioners."
More details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Research
Illustrations
XII, 324 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 19 mm
Weight
511 gr
ISBN-13
978-0-387-71823-1 (9780387718231)
DOI
10.1007/b97670
Schweitzer Classification
Other editions
Additional editions

Robert G. Cowell | Philip Dawid | Steffen L. Lauritzen
Probabilistic Networks and Expert Systems
Exact Computational Methods for Bayesian Networks
E-Book
05/2006
Springer
€117.69
Available for download

Robert G. Cowell | Philip Dawid | Steffen L. Lauritzen
Probabilistic Networks and Expert Systems
Exact Computational Methods for Bayesian Networks
Book
06/1999
Springer
€181.89
Shipment within 5-7 days
Content
Logic, Uncertainty, and Probability.- Building and Using Probabilistic Networks.- Graph Theory.- Markov Properties on Graphs.- Discrete Networks.- Gaussian and Mixed Discrete-Gaussian Networks.- Discrete Multistage Decision Networks.- Learning About Probabilities.- Checking Models Against Data.- Structural Learning.