RAraister·Oct 15, 2026
In Show HN: Markov Chains for Programmers – free book (2nd ed.) with C codeczekster
markov
C2022–2026
czekster / markov
markov
Materials for book: "Markov Chains for programmers"
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| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2026 (partial) | 2026-01-01 00:00:00 UTC | 2027-01-01 00:00:00 UTC | 2 | 1 | 1 |
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3 sourcesShow HN: Markov Chains for Programmers – free book (2nd ed.) with C code
I have just finished reviewing the 2nd edition for my Markov Chains book.
This for programmers who want Markov chains explained through code, not proofs. You build chains as matrices and solve them in C: power method, steady state for DTMCs/CTMCs, simulation to check the numbers.
2nd edition just out: errata fixed, new material on uniformised/embedded
DTMCs, and the code now has tests and CI.
Repo has the PDF (CC BY), C solutions (GPLv3), and ~14 models (Land of Oz,
mouse maze, a Shakespeare sonnet...) also in PRISM, MATLAB and Excel.
Errata and feedback welcome.
5 points · Open discussionI would try to grasp the 'random surfer' idea, that is modeled by a Markov Chain. A nice free book is Markov Chain for Programmers [1]. A discrete time Markov Chain boils down to a conditional probability that boils down to a matrix, and a steady distribution boils down to an eigenvalue 1 eigenvector of it, which determines PageRank. Then one can jump to 'The $25,000,000,000 Eigenvector: The Linear Algebra behind Google'.
[1] https://github.com/czekster/markov
[2] https://doi.org/10.1137/050623280
EDIT: admitedly there's much more to it
In Ask HN: I want to dive into how to make search engines