Paperback. Condition: New. 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 # LU-9781009340977

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Bayesian Social Science Statistics: Volume 2

Gill, Jeff (american University);bao, Le (city University Of Hong Kong)

Published by Cambridge University Press, 2026

ISBN 10: 1009340972 ISBN 13: 9781009340977

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Bayesian Social Science Statistics: Volume 2 (Paperback)

Jeff Gill

Published by Cambridge University Press, Cambridge, 2026

ISBN 10: 1009340972 ISBN 13: 9781009340977

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Paperback. Condition: new. Paperback. 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. 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 item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781009340977

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