Chain Event Graphs
chapman & hall/crc computer science and data analysis series
$173.60
- Hardcover
254 pages
- Release Date
30 January 2018
Summary
Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of discrete Bayesian Networks so that they apply in a much more general setting As the first book on Chain Event Graphs, this monograph is expected to become a landmark work on the use of event trees and colo…
Book Details
| ISBN-13: | 9781498729604 |
|---|---|
| ISBN-10: | 1498729606 |
| Author: | Rodrigo A. Collazo, Christiane Goergen, Jim Q. Smith |
| Publisher: | Taylor & Francis Inc |
| Imprint: | CRC Press Inc |
| Format: | Hardcover |
| Number of Pages: | 254 |
| Release Date: | 30 January 2018 |
| Weight: | 700g |
| Dimensions: | 234mm x 156mm |
| Series: | Chapman & Hall/CRC Computer Science & Data Analysis |
What They're Saying
Critics Review
“Statisticians Collazo, G
“Statisticians Collazo, Görgen, and Smith provide a thorough introduction to the methodology of chain event graphs. The authors present background on discrete statistical modeling and the use of Bayesian inference. The chain event graph method is shown to be less restrictive than that of Bayesian networks, though it represents something of a generalization of that method. Beginning with an event tree, the chain event graph is a graphical representation that can represent a process of developing events. The authors present an array of examples to illustrate the concepts, and exercises are scattered throughout the text. Included with the book’s purchase is access to software to create these models. Readers interested in this subject may also wish to consult the works of Judea Pearl, who developed Bayesian Networks and promoted the use of a probabilistic approach to the field of artificial intelligence (see, for example, Causality: Models, Reasoning, and Inference, CH, Mar’10, 47-3771).” ~CHOICE, R. L. Pour, emeritus, Emory and Henry College Summing Up: Recommended. Upper-division undergraduates through faculty and professionals.
“Statisticians Collazo, Görgen, and Smith provide a thorough introduction to the methodology of chain event graphs. The authors present background on discrete statistical modeling and the use of Bayesian inference. The chain event graph method is shown to be less restrictive than that of Bayesian networks, though it represents something of a generalization of that method. Beginning with an event tree, the chain event graph is a graphical representation that can represent a process of developing events. The authors present an array of examples to illustrate the concepts, and exercises are scattered throughout the text. Included with the book’s purchase is access to software to create these models. Readers interested in this subject may also wish to consult the works of Judea Pearl, who developed Bayesian Networks and promoted the use of a probabilistic approach to the field of artificial intelligence (see, for example, Causality: Models, Reasoning, and Inference, CH, Mar’10, 47-3771).” ~CHOICE, R. L. Pour, emeritus, Emory and Henry College Summing Up: Recommended. Upper-division undergraduates through faculty and professionals.
About The Author
Rodrigo A. Collazo
Rodrigo A. Collazo, Christiane Goergen, Jim Q. Smith
Returns
This item is eligible for free returns within 30 days of delivery. See our returns policy for further details.