Chain event graphs

By:

Collazo, Rodrigo A

Contributor(s):

Material type: Text

TextSeries: Chapman and Hall/CRC computer science and data analysis seriesPublication details: CRC Press 2018 FloridaDescription: xx, 233p. With indexISBN:

9781498729604

Subject(s):

DDC classification:

519.542 C6C4

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 coloured probability trees in statistics, and to lead to the increased use of such tree models to describe hypotheses about how events might unfold. Features: introduces a new and exciting discrete graphical model based on an event tree focusses on illustrating inferential techniques, making its methodology accessible to a very broad audience and, most importantly, to practitioners illustrated by a wide range of examples, encompassing important present and future applications includes exercises to test comprehension and can easily be used as a course book introduces relevant software packages https://www.crcpress.com/Chain-Event-Graphs/Collazo-Goergen-Smith/p/book/9781498729604

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Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad General Stacks	Non-fiction	519.542 C6C4 (Browse shelf(Opens below))	Available		199224

Total holds: 0

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Previous	519.54 R6M2 Mathematical statistics: an introduction to likelihood based inference	519.542 B7B2 Bayesian demographic estimation and forecasting	519.542 C4B2 Bayesian statistics for experimental scientists: a general introduction using distribution-free methods	519.542 C6C4 Chain event graphs	519.542 F3R4 Risk assessment and decision analysis with Bayesian networks /	519.542 H2 Handbook of approximate bayesian computation	519.542 M2S8-2020 Statistical rethinking: a Bayesian course with examples in R and Stan	Next

Table of Contents

1.Introduction

2.Bayesian inference using graphs

3.The Chain Event Graph

4.Reasoning with a CEG

5.Estimation and propagation on a given CEG

6.Model selection for CEGs

7.How to model with a CEG: a real-world application

8.Causal inference using CEGs

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 coloured probability trees in statistics, and to lead to the increased use of such tree models to describe hypotheses about how events might unfold.
Features:
introduces a new and exciting discrete graphical model based on an event tree
focusses on illustrating inferential techniques, making its methodology accessible to a very broad audience and, most importantly, to practitioners
illustrated by a wide range of examples, encompassing important present and future applications
includes exercises to test comprehension and can easily be used as a course book
introduces relevant software packages

https://www.crcpress.com/Chain-Event-Graphs/Collazo-Goergen-Smith/p/book/9781498729604

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