Book Details
Format
Hardback or Cased Book
ISBN-10
1009598449
ISBN-13
9781009598446
Publisher
Cambridge University Press
Imprint
Cambridge University Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Mar 19th, 2026
Print length
116 Pages
Weight
324 grams
Dimensions
16.00 x 23.70 x 1.50 cms
Ksh 10,150.00
Manufactured on Demand
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This 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 introduces the basics of Bayesian regression modeling using modern computational tools. This Element 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.
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