Bayesian Social Science Statistics: Volume 2
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Bayesian Social Science Statistics: Volume 2
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See moreThis Element introduces the basics of Bayesian regression modeling using modern computational tools and assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level. The sections cover theoretical principles and real-world applications to provide motivation and intuition.
This Element is an introduction to Bayesian regression, combining theory and application with R and Python code for learners.
This Element introduces the basics of Bayesian regression modeling using modern computational tools. Bayesian Social Science Statistics: Volume 2 only assumes that the reader has taken a basic statistics course and has seen Bayesian inference at the introductory level of Gill and Bao (2024).
Some matrix algebra knowledge is assumed, but the authors walk carefully through the necessary structures at the start of this Element. At the end of the process, readers will fully understand how Bayesian regression models are developed and estimated, including linear and nonlinear versions.
The sections cover theoretical principles and real-world applications in order to provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in R and Python is provided throughout.
Series: Elements in Quantitative and Computational Methods for the Social Sciences
View allBook Details
INFORMATION
ISBN: 9781009340977
Publisher: Cambridge University Press
Format: Paperback / softback
Date Published: 19 March 2026
Country: United Kingdom
Imprint: Cambridge University Press
Illustration: Worked examples or Exercises
Audience: General / adult
DIMENSIONS
Weight: 300g
Pages: 116
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