Bayesian Social Science Statistics

Jeff Gill

ISBN 10: 1009340972 / ISBN 13: 9781009340977

Published by Cambridge University Press, 2026

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781009340977

Bibliographic Details

• Title: Bayesian Social Science Statistics
• Publisher: Cambridge University Press
• Publication Date: 2026
• Language: English
• ISBN 10: 1009340972
• ISBN 13: 9781009340977
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 7D millimeters 229 millimeters width by 152 millimeters height by 7 millimeters depth
• Weight: 181 grams

About this title

Synopsis

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.