I know what Markov Chains are (the probabilities for the next state of the chain is dependent only on the current state)
What I would prefer to know is why are they more useful than a more nuanced model with non-fixed probabilities, or memory of previous states? - given that these are not really harder to simulate.
It seems to me that Markov Chains are very often used as an inappropriate simplification.
Is there some mathematical advantage to using them?
What I would prefer to know is why are they more useful than a more nuanced model with non-fixed probabilities, or memory of previous states? - given that these are not really harder to simulate.
It seems to me that Markov Chains are very often used as an inappropriate simplification.
Is there some mathematical advantage to using them?