General purpose repo for learning statistics, Bayesian reasoning and methods, Python examples
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updated Oct 7, 2026
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README
Bayes, Stats, Cards and Computers
A self-study project in statistics, Bayesian methods and computation, in Python. Lessons are anchored to real problems where possible, including a Pokémon card price project.
The first course here is a set of Jupyter workbooks that follow David Robinson's Introduction to Empirical Bayes: Examples from Baseball Statistics, with all the code in Python.
What's here
| Path | What it is |
|---|---|
empirical_bayes/ | 13 lesson notebooks on empirical Bayes, using baseball batting averages. See its README for the lesson list. |
empirical_bayes/eb_data.py | Downloads the baseball data and builds the tables each lesson starts from. |
LEARNING_STATS_HANDOFF.md | Working notes for the wider learning plan: resources, environment notes, topics so far and open items. |
The lessons
The notebooks are workbooks, not solutions. Each one starts with a topic, a question, a goal, a contents list and tags, then a setup cell that's ready to run. After that come prompts:
- Your turn: a task, with empty code cells for you to fill in;
- Check: values to compare your results against;
- Reflect: a question to answer in a sentence or two;
- Card connection: an optional exercise applying the idea to trading-card data (simulated).
Lessons 01–12 follow Chapters 2–13 of the book: the beta distribution, empirical Bayes estimation, credible intervals, false discovery rates, A/B testing, beta-binomial regression, hierarchical priors, mixture models and EM, the Dirichlet-multinomial, building a small toolkit, and simulation. Lesson 13 goes beyond the book and compares empirical Bayes with a full Bayesian model in PyMC.
You'll need the book
The book isn't included here. It's a paid ebook, available on Amazon and other stores. The blog series it grew out of is free on David Robinson's blog, Variance Explained. Its R source is at dgrtwo/empirical-bayes-book.
Setup
You need Python 3.11 or newer with Jupyter. With Anaconda or Miniconda, create an environment from an Anaconda Prompt (or any terminal):
conda create -n pymc -c conda-forge python=3.12 pymc nutpie arviz jupyterlab pandas scipy matplotlib patsy
conda activate pymc
Lessons 01–12 need only numpy, pandas, scipy, matplotlib and patsy. PyMC, nutpie and ArviZ are for Lesson 13, which is written for PyMC 6 and ArviZ 1. Older tutorials use different function names.
Then start Jupyter from the activated environment:
cd path\to\BayesStatsCardsandComputers\empirical_bayes
jupyter lab
Open 01_beta_distribution.ipynb and work through the lessons in order. Later lessons reuse results and functions from earlier ones.
Data
The baseball data is the Lahman Baseball Database (CC BY-SA 3.0). It isn't stored in the repo: Lesson 02's setup cell downloads it into empirical_bayes/data/lahman/ the first time you run it (about 10 MB). If the download fails, download the CSV version from the SABR page and unzip it into that folder.
The card-project exercises use simulated data only, because the real card price data comes from sources whose terms don't allow republishing it.
Credits
- David Robinson, Introduction to Empirical Bayes: Examples from Baseball Statistics (2017). The lessons follow its structure and examples.
- Sean Lahman's Baseball Database, via SABR and the Chadwick Bureau.
Similar repos
General purpose repo for learning statistics, Bayesian reasoning and methods, Python examples
Jupyter Notebook
0
8 commits
updated Oct 7, 2026
See what people are saying
README
Bayes, Stats, Cards and Computers
A self-study project in statistics, Bayesian methods and computation, in Python. Lessons are anchored to real problems where possible, including a Pokémon card price project.
The first course here is a set of Jupyter workbooks that follow David Robinson's Introduction to Empirical Bayes: Examples from Baseball Statistics, with all the code in Python.
What's here
| Path | What it is |
|---|---|
empirical_bayes/ | 13 lesson notebooks on empirical Bayes, using baseball batting averages. See its README for the lesson list. |
empirical_bayes/eb_data.py | Downloads the baseball data and builds the tables each lesson starts from. |
LEARNING_STATS_HANDOFF.md | Working notes for the wider learning plan: resources, environment notes, topics so far and open items. |
The lessons
The notebooks are workbooks, not solutions. Each one starts with a topic, a question, a goal, a contents list and tags, then a setup cell that's ready to run. After that come prompts:
- Your turn: a task, with empty code cells for you to fill in;
- Check: values to compare your results against;
- Reflect: a question to answer in a sentence or two;
- Card connection: an optional exercise applying the idea to trading-card data (simulated).
Lessons 01–12 follow Chapters 2–13 of the book: the beta distribution, empirical Bayes estimation, credible intervals, false discovery rates, A/B testing, beta-binomial regression, hierarchical priors, mixture models and EM, the Dirichlet-multinomial, building a small toolkit, and simulation. Lesson 13 goes beyond the book and compares empirical Bayes with a full Bayesian model in PyMC.
You'll need the book
The book isn't included here. It's a paid ebook, available on Amazon and other stores. The blog series it grew out of is free on David Robinson's blog, Variance Explained. Its R source is at dgrtwo/empirical-bayes-book.
Setup
You need Python 3.11 or newer with Jupyter. With Anaconda or Miniconda, create an environment from an Anaconda Prompt (or any terminal):
conda create -n pymc -c conda-forge python=3.12 pymc nutpie arviz jupyterlab pandas scipy matplotlib patsy
conda activate pymc
Lessons 01–12 need only numpy, pandas, scipy, matplotlib and patsy. PyMC, nutpie and ArviZ are for Lesson 13, which is written for PyMC 6 and ArviZ 1. Older tutorials use different function names.
Then start Jupyter from the activated environment:
cd path\to\BayesStatsCardsandComputers\empirical_bayes
jupyter lab
Open 01_beta_distribution.ipynb and work through the lessons in order. Later lessons reuse results and functions from earlier ones.
Data
The baseball data is the Lahman Baseball Database (CC BY-SA 3.0). It isn't stored in the repo: Lesson 02's setup cell downloads it into empirical_bayes/data/lahman/ the first time you run it (about 10 MB). If the download fails, download the CSV version from the SABR page and unzip it into that folder.
The card-project exercises use simulated data only, because the real card price data comes from sources whose terms don't allow republishing it.
Credits
- David Robinson, Introduction to Empirical Bayes: Examples from Baseball Statistics (2017). The lessons follow its structure and examples.
- Sean Lahman's Baseball Database, via SABR and the Chadwick Bureau.