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Markov Chains explained

Started by vladir, Mar 22, 07:07 AM 2016

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vladir

"In God we trust; all others must bring data", W. Edwards Deming

NextYear

Thanks, a little bit of science shouldn't kill nobody...

Rourke

Very interesting post Vladir :-)

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

QuoteWe 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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