Explore Bayesian inference concepts in this comprehensive lecture on statistical rethinking. Delve into Bayesian updating, sampling posterior distributions, and computing posterior and prior predictive distributions. Begin with an introduction to the "Garden of forking data" concept, followed by a practical demonstration using globe tossing. After a brief intermission, examine the formalities of Bayesian inference, learn about grid approximation techniques, and investigate posterior predictive distributions. Conclude with a summary of key takeaways. Access additional course materials on GitHub for further study and enhancement of your understanding of Bayesian statistical methods.
Statistical Rethinking 2022 - Bayesian Inference
Overview
Syllabus
Introduction
Garden of forking data
Globe tossing
Intermission
Formalities
Grid approximation
Posterior predictive distributions
Summary
Taught by
Richard McElreath
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