Markov Chains explained

[vladir]

link:://setosa.io/ev/markov-chains/

[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:

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.