Bayesian social science statistics: from the very beginning

By:

Jeff, Gill

Contributor(s):

Bao, Le

Material type: Text

TextSeries: Elements in quantitative and computational methods for social sciencePublication details: Cambridge University Press New York 2024Description: 99 pISBN:

9781009341196

Subject(s):

Bayesian statistical--Decision theory

DDC classification:

519.5 JEF

Summary: 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 (https://www.cambridge.org/core/elements/abs/bayesian-social-science-statistics/84023AFD66232729E7CE7B4F12D8773D)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.5 JEF (Browse shelf(Opens below))	1	Available		007984

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

(https://www.cambridge.org/core/elements/abs/bayesian-social-science-statistics/84023AFD66232729E7CE7B4F12D8773D)

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