Bayesian networks : with examples in R / by Marco Scutari and Jean-Baptiste Denis.

Format:

Book

Author/Creator:

Scutari, Marco, author.

Denis, Jean-Baptiste, author.

Series:

Texts in statistical science.

Texts in statistical science

Language:

English

Subjects (All):

Bayesian statistical decision theory.

R (Computer program language).

Physical Description:

1 online resource (239 p.)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : Chapman and Hall/CRC, an imprint of Taylor and Francis, 2014.

Summary:

Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained. Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets.

Contents:

Front Cover; Contents; Preface; Chapter 1: The Discrete Case: Multinomial Bayesian Networks; Chapter 2: The Continuous Case: Gaussian Bayesian Networks; Chapter 3: More Complex Cases: Hybrid Bayesian Networks; Chapter 4: Theory and Algorithms for Bayesian Networks; Chapter 5: Software for Bayesian Networks; Chapter 6: Real-World Applications of Bayesian Networks; Appendix A: Graph Theory; Appendix B: Probability Distributions; Appendix C: A Note about Bayesian Networks; Glossary; Solutions; Bibliography; Back Cover

Notes:

Description based upon print version of record.

Includes bibliographical references.

Description based on online resource; title from PDF title page (ebrary, viewed May 5, 2016).

ISBN:

0-429-19624-5

1-4822-2559-X

9780429196249

OCLC:

870289609