Markov chain distribution and convergence
three.js and WebGL · 3D · MIT
- What it does
Separate a single random walker from the distribution over states, and compare convergence or oscillation under a declared transition matrix and initial distribution. A written recipe (probability-statistics; part sourced, part authored): the parts to model, the one state that drives them, what must stay true, how it should move and its known limits. Text for an agent to build from, not code; its own limits say no scene was run for it.
- Get it
python3 scripts/resolve.py recipe.markov-chain-convergence
- Needs (the whole pack)
Nothing to read the skill. Python 3 for the catalog helpers. Node with pnpm or npm for the starter and the examples. Optional: Playwright for its capture script, and Blender 4.2 or newer for its baked hero tier. The six anatomy studies need 169 MB of meshes fetched separately.
- License
MIT
- From