Systems that hop between states by fixed probabilities forget where they started — they settle into one stationary distribution. Watch it converge.
Markov ChainLive
transitions → stationary distribution
A100.0%
B0.0%
C0.0%
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A system hops between states by fixed probabilities. From any start it converges to the same stationary distribution — the math behind PageRank and queueing.
Reading this result: State A has the stickiest self-loop (0.70), so it keeps the largest share of the stationary distribution no matter where you start.
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How it works
A Markov chain moves between states according to a transition matrix. No matter the starting state, an ergodic chain converges to a unique stationary distribution — the principle behind Google PageRank, queueing theory, and Markov-chain Monte Carlo.
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The math, the assumptions, real-world uses, or a code translation — explained for this exact simulation.
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The solver uses established numerical methods, but results are for research and educational purposes and should be validated against experiment or professional review before you rely on them.