Full Description
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.
Contents
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.
