Author Topic: Markov Chains explained  (Read 704 times)

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vladir

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Markov Chains explained
« on: March 22, 2016, 11:07:54 AM »
"In God we trust; all others must bring data", W. Edwards Deming

NextYear

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Re: Markov Chains explained
« Reply #1 on: March 22, 2016, 02:24:23 PM »
Thanks, a little bit of science shouldn't kill nobody...

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Re: Markov Chains explained
« Reply #1 on: March 22, 2016, 02:24:23 PM »

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Rourke

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Re: Markov Chains explained
« Reply #2 on: March 22, 2016, 05:34:03 PM »
Very interesting post Vladir :-)

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

Quote
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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