We begin with a famous example then describe the

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Unformatted text preview: feature of Markov chains Example 1.1. Gambler’s ruin. Consider a gambling game in which on any turn you win $1 with probability p = 0.4 or lose $1 with probability 1 p = 0.6. Suppose further that you adopt the rule that you quit playing if your fortune reaches $N . Of course, if your fortune reaches $0 the casino makes you stop. Let Xn be the amount of money you have after n plays. Your fortune, Xn has the “Markov property.” In words, this means that given the current state, Xn , any other information about the past is irrelevant for predicting the next state Xn+1 . To check this for the gambler’s ruin chain, we note that if you are still playing at time n, i.e., your fortune Xn = i with 0 < i < N , then for any possible history of your wealth in 1 , in 2 , . . . i1 , i0 P (Xn+1 = i + 1|Xn = i, Xn 1 = in 1 , . . . X0 = i0 ) = 0.4 since to increase your wealth by one unit you have to win your next bet. Here we have used P (B |A) for the conditional probability of the event B given that A occurs. Recall that this is defined by P (B |A) = P (B \ A) P (A) If yo...
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This document was uploaded on 03/06/2014 for the course MATH 4740 at Cornell.

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