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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

MCMC Sampling I

3. MCMC Sampling I#

In this section we introduce Markov Chain Monte Carlo sampling. (More later.)

Good references

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2.9. Follow-up: fluctuation trends with # of points and data errors

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3.1. Lecture 7

By Dick Furnstahl and Daniel Phillips

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