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This course introduces linear regression from a Bayesian perspective, covering generative and statistical models, validation, analysis, and posterior predictions.
Syllabus
Introduction
Why normal?
Flow
Language for modeling
Linear models, generative
Intermission
Linear models, statistical
Validation and analysis
Posterior predictions
Summary
Taught by
Richard McElreath