Marco Scutari, Jean-Baptiste Denis

Bayesian Networks (eBook, ePUB)

With Examples in R

• Format: ePub

Produktbeschreibung

The book introduces Bayesian networks using simple yet meaningful examples. Discrete Bayesian networks are described first followed by Gaussian Bayesian networks and mixed networks. All steps in learning are illustrated with R code.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 274
• Erscheinungstermin: 28. Juli 2021
• Englisch
• ISBN-13: 9781000410396
• Artikelnr.: 61973109

Autorenporträt

Marco Scutari is a Senior Lecturer at Istituto Dalle Molle di Studisull'Intelligenza Artificiale (IDSIA), Switzerland. He has held positions in Statistics, Statistical Genetics and Machine Learning in the UK and Switzerland since completing his Ph.D. in Statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering.

Jean-Baptiste Denis was formerly appointed as a statistician and modeller at the "Mathematics and Applied Informatics from Genome to Environment" unit of the French National Research Institute for Agriculture, Food and Environment. His main research interests were the modelling of two-way tables and Bayesian approaches, especially applied to genotype-by-environment interactions and microbiological food safety.

Inhaltsangabe

1. The Discrete Case: Multinomial Bayesian Networks. 2. The Discrete Case:
Multinomial Bayesian Networks. 3. The Mixed Case: Conditional Gaussian
Bayesian Networks. 4. Time Series: Dynamic Bayesian Networks. 5. More
Complex Cases: General Bayesian Networks. 6. Theory and Algorithms for
Bayesian Networks. 7. Software for Bayesian Networks. 8. Real-World
Applications of Bayesian Networks.