Bayesian Social Science Statistics

From the Very Beginning

Jeff Gill (American University), Le Bao (Georgetown University)

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English

Cambridge University Press
24 October 2024

Research methods: general; Social research & statistics; Mathematical & statistical software

Series: Elements in Quantitative and Computational Methods for the Social Sciences

In this Element, the authors introduce Bayesian probability and inference for social science students and practitioners starting from the absolute beginning and walk readers steadily through the Element. No previous knowledge is required other than that in a basic statistics course. At the end of the process, readers will understand the core tenets of Bayesian theory and practice in a way that enables them to specify, implement, and understand models using practical social science data. Chapters will cover theoretical principles and real-world applications that provide motivation and intuition. Because Bayesian methods are intricately tied to software, code in both R and Python is provided throughout.

By: Jeff Gill (American University), Le Bao (Georgetown University)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 229mm, Width: 152mm, Spine: 6mm
Weight: 191g
ISBN: 9781009341196
ISBN 10: 1009341197
Series: Elements in Quantitative and Computational Methods for the Social Sciences
Pages: 110
Publication Date: 24 October 2024
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active

1. Introduction: the purpose and scope of this book; 2. Basic probability principles and Bayes law; 3. What is a likelihood function and why care; 4. The core of Bayesian inference: prior times likelihood; 5. Prior probabilities and the progression of human knowledge; 6. Integrals and expected value: not as scary as they look; 7. Software calculation of Bayesian models; 8. Evaluating and comparing model results; 9. Case study I: election polling and Bayesian updating; References.