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.

This Element is an introduction to Bayesian statistics for social science students and practitioners starting from the absolute beginning.