Learning from data

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 are there?
Mini-project IIIa: Bayesian optimization
Mini-project IIIb: Bayesian Neural Networks

Reference material

Bibliography
Related topics
Using Anaconda
Using GitHub
Python and Jupyter notebooks
- Python and Jupyter notebooks: part 01
- Python and Jupyter notebooks: part 02
Examples: Jupyter jb-book

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

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Model mixing¶

In this section we will go over basic features of Bayesian model averaging and model mixing. *[Note: this section is still under development.]

Good references are:

By Dick Furnstahl
© Copyright 2021.