Verwandte Artikel zu Bayesian Statistical Methods: With Applications to...

Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/Crc Texts in Statistical Science) - Hardcover

Buch 112 von 126: Chapman & Hall/CRC Texts in Statistical Science

Reich, Brian J.; Ghosh, Sujit K.

 
9781032486321: Bayesian Statistical Methods: With Applications to Machine Learning (Chapman & Hall/Crc Texts in Statistical Science)
Hardcover
ISBN 10: 1032486325 ISBN 13: 9781032486321
Verlag: Chapman and Hall/CRC, 2026

Inhaltsangabe

This book provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, it is more focused on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.

Über die Autorin bzw. den Autor

Brian J. Reich, Gertrude M. Cox Distinguished Professor of Statistics at North Carolina State University, applies Bayesian statistical methods in a variety of fields including environmental epidemiology, engineering, weather and climate. He is a Fellow of the American Statistical Association, former Editor-in-Chief of the Journal of Agricultural, Biological, and Environmental Statistics and recipient of the LeRoy & Elva Martin Teaching Award at NC State University.

Sujit K. Ghosh, Professor of Statistics at North Carolina State University, has worked in advanced research fields such as Bayesian inference, spatial statistics, survival analysis and shape-constrained inference, addressing complex inferential challenges in biomedical and environmental sciences, econometrics, and engineering. At NC State, he has been honored with the D.D. Mason Faculty Award and the Cavell Brownie Mentoring Award, reflecting his excellence in research, mentoring and teaching. His leadership includes impactful service as Program Director at NSF's Division of Mathematical Sciences, Deputy Director at SAMSI and President of the IISA.

Von der hinteren Coverseite

This book provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. Compared to others, it is more focused on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Chapman and Hall/CRC
Erscheinungsdatum
2026
Sprache
Englisch
ISBN 10 
1032486325
ISBN 13 
9781032486321
Einband
Gebundene Ausgabe
Auflage
2
Anzahl der Seiten
360
Kontakt zum Hersteller
preigu GmbH & Co. KG
mail@preigu.de

Lengericher Landstr. 19
Osnabrück
Niedersachsen
49078
Deutschland