Chapter: An Introduction to Bayesian Networks and Their Contemporary Applications

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Home > Innovations in Bayesian Networks: Theory and Applications > An Introduction to Bayesian Networks and Their Contemporary Applications

Innovations in Bayesian Networks: Theory and Applications |

Dawn E. Holmes

Springer Berlin Heidelberg

Innovations in Bayesian Networks: Theory and Applications

An Introduction to Bayesian Networks and Their Contemporary Applications

Chapter Info

Authors

Daryle Niedermayer

ARI Id

1664968515280_4275094

Access

Not Available Free

Pages

117-130

DOI

10.1007/978-3-540-85066-3_5

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-540-85066-3_5

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Chapters/Headings	Author(s)	Pages	Info
Front Matter		I-X
Introduction to Bayesian Networks	Dawn E. Holmes; Lakhmi C. Jain	1-5
A Polemic for Bayesian Statistics	Richard E. Neapolitan	7-32
A Tutorial on Learning with Bayesian Networks	David Heckerman	33-82
The Causal Interpretation of Bayesian Networks	Kevin B. Korb; Ann E. Nicholson	83-116
An Introduction to Bayesian Networks and Their Contemporary Applications	Daryle Niedermayer	117-130
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer	Sylvia Nagl; Matt Williams; Jon Williamson	131-167
Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks	Xia Jiang; Michael M. Wagner; Gregory F. Cooper	169-185
An Information-Geometric Approach to Learning Bayesian Network Topologies from Data	Eitel J. M. Lauría	187-217
Causal Graphical Models with Latent Variables: Learning and Inference	Philippe Leray; Stijn Meganek; Sam Maes; Bernard Manderick	219-249
Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem	M. Julia Flores; José A. Gámez; Serafín Moral	251-280
Toward a Generalized Bayesian Network	Dawn E. Holmes	281-288
A Survey of First-Order Probabilistic Models	Rodrigo de Salvo Braz; Eyal Amir; Dan Roth	289-317
Back Matter		319-321
Chapters/Headings	Author(s)	Pages	Info

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