Chains, Walks & Sampling

The question you can’t sum. Chibany wants an answer about a whole semester of bentos — and for the first time, no formula gives it. This Part’s move: when you can’t compute, wander and count. Chibany wanders the campus (Markov chains), the campus web (random walks and PageRank), their own memory (search as a censored walk), and finally learns to answer any question by sampling — Monte Carlo, particle filtering, and MCMC — closing with the question of whether minds sample too.

graph LR
    A[Markov<br>Chains] --> B[Random Walks<br>on Networks]
    B --> C[Memory<br>Search 🧠]
    C --> D[Monte<br>Carlo]
    D --> E[Particle<br>Filtering]
    E --> F[MCMC]
    F --> G[Sampling<br>the Mind 🧠]

Chapters

• Markov Chains: The Future Forgets the Past
• Random Walks on Networks
• Memory Search as a Random Walk
• Monte Carlo: Estimating by Sampling
• Particle Filtering: Yesterday's Posterior Is Today's Prior
• Markov Chain Monte Carlo: Designing a Chain to Hit a Target
• Sampling the Mind: People and the Kemp Hierarchy

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