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Re: Markov Chains explained

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Very interesting post Vladir :-)

However... I don't think you can apply the following to roulette:

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We can minic this "stickyness" with a two-state Markov chain. When the Markov chain is in state "R", it has a 0.9 probability of staying put and a 0.1 chance of leaving for the "S" state. Likewise, "S" state has 0.9 probability of staying put and a 0.1 chance of transitioning to the "R" state.

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