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Bayesian Reasoning in Data Science (DATA 644)

Bayesian Reasoning in Data Science (PhD Course)

Lectures (integrate with notebooks below)

Bayes Theorem, priors, likelihood, posterior, distributions - Lec 1 (1/25/2024)
Bayes Theorem, subjective and objective interpretations of probability, Exercises - Lec 2 (1/30/2024)
Thinking Probabilistically, Causes and Effects , True and Measured; Credible Intervals, ROPE, HDI - Lec 3 (2/1/2024)
Inference and Intro to Probabilistic Programming, PyMC; Credible Intervals, ROPE, HDI - Lec 4 (2/6/2024)
Inference and Intro to Probabilistic Programming, PyMC; Credible Intervals, ROPE, HDI - Lec 5 (2/8/2024)
Gaussian Inference; t-Student; Update of Beliefs - Lec 6 (2/13/2024)
Bayesian Linear Regression; Robust Regression - Lec 7 (2/15/2024)
Bayesian Polynomial Regression, Posterior Predictive Checks - Lec 8 (2/20/2024)
Bayesian Logistic Regression and Softmax - Lec 9 (2/22/2024), Lec 10 (2/27/2024), Lec 11 (2/29/2024)
Bayesian Model Comparison and Information Criteria - Lec 12 (3/5/2024)
Bayes Factors, sequential MC - Lec 13 (3/7/2024)
MCMC from scratch - Lec 14 (3/19/2024)
Intro to Gaussian Processes - Lec 15 (3/21/2024), Lec 16 (3/26/2024), Lec 17 (3/28/2024)
Introduction to Bayes Networks - Lec 20 (4/9/2024)
Introduction to Bayesian Neural Networks - Lec 22 (4/16/2024)
BNN (continue) - Lec 24 (4/25/2024)
Finale - Lec 25 (5/2/2024)

Notebooks

Bayes Theorem (2/1/2024)
Thinking Probabilistically (2/1/2024)
Intro to Probabilistic Programming (2/6/2024)
An (Over-)Simplified Intro to MCMC (2/8/2024)
Bayesian Inference with Gaussian and t-Student models, update of beliefs (2/13/2024)
Bayesian Linear Regression; Robust Inference (2/15/2024)
Bayesian Polynomial Regression, Posterior Predictive Checks and Comparison with Observations (2/20/2024)
Bayesian Logistic Regression (2/22/2024)
Bayesian Softmax (2/27/2024)
Model Comparison, Information Criteria (3/5/2024)
Model Comparison, Bayes Factors (3/7/2024)
Building MCMC from scratch; a closer look at samplers (3/19/2024)
Gaussian Processes (3/26/2024)
Multi-Objective Optimization (4/4/2024)
Bayesian Networks (4/9/2024) and (4/11/2024)
Bayesian Neural Networks (4/16/2024)
Bayesian Convolutional Neural Network (4/25/2024)

Assignments

Assignment 1 - PDF (Questions 1 and 3)
Assignment 1 - notebook (Question 2)
Assignment 2 - PDF
Assignment 2 - notebook (All Questions)
Assignment 3 - PDF
Assignment 3 - notebook (All Questions)
Assignment 4 - PDF
Assignment 4 - notebook (Approach 1)
Assignment 4 - notebook (Approach 2)
Assignment 5 - PDF
Assignment 5 - notebook (All Questions)

Question and Answers

Q&A Shared Google Doc (used in class)

Projects

Mini-project (pyscript + PyMC)
Bayesian A/B testing (pyscript) by R. Gupta, BRDS class 2022

Additional resources

MCMC examples
References

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By Cristiano Fanelli
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