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About this Jupyter Book

Course overview

Objectives

Topics

1. Basics of Bayesian statistics
2. Bayesian parameter estimation
3. MCMC sampling I
4. Why Bayes is better
5. Model selection
6. MCMC sampling II
7. Gaussian processes
8. Assigning probabilities
9. Machine learning: Bayesian methods
10. PCA, SVD, and all that
- 10.1. Lecture 25
- 10.2. Linear algebra games including SVD for PCA

Mini-projects

Mini-project I: Parameter estimation for a toy model of an EFT
Mini-project IIa: Model selection basics
Mini-project IIb: How many lines?
Mini-project IIIa: Bayesian optimization
Mini-project IIIb: Bayesian Neural Networks

Reference material

Bibliography
Using Anaconda
Using GitHub
Python and Jupyter notebooks
Examples: Jupyter jb-book
Related topics
- Student t from Gaussians
- QBism

Notebook keys

Checking the sum and product rules, and their consequences Key
Standard medical example by applying Bayesian rules of probability Key
Radioactive lighthouse problem Key

Repository
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By Dick Furnstahl and Daniel Phillips

© Copyright 2024.