Foundations: Counting Lunches

Will there be tonkatsu today? It is April, a new school year at Chiba Tech, and the campus mascot Chibany has one consuming question about the two bentos students bring them every day. This Part builds all of probability out of that question — by counting. Sets of possible days, events as subsets, conditioning as crossing out possibilities, and Bayes’ rule as the inevitable arithmetic of updating.

Most probability courses jump straight into formulas. Here, probability is fancy counting: What are all the possibilities? Which ones include tonkatsu? What’s the ratio? That perspective makes conditional probability, Bayes’ rule, and everything after them feel natural instead of mysterious — and it is exactly the mental model that probabilistic programming runs on. No prior math background required.

graph LR
    A[Chibany<br>is Hungry] --> B[Probability<br>& Counting]
    B --> C[Conditional<br>Probability]
    C --> D[Bayes'<br>Theorem]
    D --> E[Glossary]

    style B fill:#4a9eff,color:#fff
    style C fill:#4a9eff,color:#fff
    style D fill:#4a9eff,color:#fff

Chapters

New to the book? The Start Here guide explains the two reading tracks and how the Parts fit together.

This project is generously funded by the Japanese Probabilistic Computing Consortium Association (JPCCA).