Explore the complexities of missing data analysis in this comprehensive lecture from the Statistical Rethinking 2022 series. Delve into the representation of missing data in Directed Acyclic Graphs (DAGs), understand the concepts and implementation of Bayesian imputation, and examine a complete Stan example. Learn about censored observations and gain insights into handling incomplete datasets effectively. Access accompanying course materials, including slides, on GitHub for a deeper understanding of the topics covered.
Statistical Rethinking 2022 - Missing Data
Overview
Syllabus
Introduction
Missing data in DAGs
Bayesian imputation, concepts
Bayesian imputation, code
Complete Stan example
Summary and outlook
Censored observations
Taught by
Richard McElreath
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