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Bayesian Reasoning in Data Science
Bayesian Reasoning in Data Science
Lectures
mod1 - Course Structure; Real-world Bayesian applications; Causes and effects; True and Measured - Lecture 1 (9/1/2022)
mod1 - Bayes Theorem; Exercises; Thinking Probabilistically - Lecture 2 (9/6/2022)
mod1 - Credible Intervals; ROPE; HDI - Lecture 6 and 7 (9/20 and 22/2022)
mod2 - Bayesian Linear Regression - Lecture 10 and 11 (10/04 and 10/05)
Notebooks
(mod1 - part1) Thinking Probabilistically
(mod1 - part2) Intro to Probabilistic Programming
(mod1 - part3) Gaussian Inferences - t-Student
(mod2 - part1) Bayesian Linear Regression
(mod2 - part2) Bayesian Polynomial Regression
(mod2 - part2b) Bayesian Logistic Regression
(mod2 - part3) Bayesian Softmax Regression
(mod3 - part1) Intro to Gaussian Processes
(mod3 - part1b) Bayesian Optimization
(mod3 - part2) Bayesian A/B Testing
(mod3 - part3) Markov Chain Monte Carlo (MCMC)
(mod3 - part3 extra) MCMC Applications, De-noising Image
(mod3 - part3b) MCMC Diagnostics
Assignments
Assignments of module 3 (part1, part2, part3)
Mini-Projects
Mini-project (pyscript + PyMC)
Bayesian A/B testing (pyscript) by R. Gupta, BRDS class 2022
Additional resources
References
repository
open issue
Index
