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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
Open issue

.md

Related topics

Related topics#

Here we gather information on various topics in some way connected to the themes of these notes but which do not (at present) fit into the flow of the course.

previous

Tests of Jupyter Book features

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Student t distribution as a mixture of Gaussians

By Dick Furnstahl and Daniel Phillips

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