Chapter 4: Problem 72
For a time reversible Markov chain, argue that the rate at which transitions from \(i\) to \(j\) to \(k\) occur must equal the rate at which transitions from \(k\) to \(j\) to \(i\) occur.
Chapter 4: Problem 72
For a time reversible Markov chain, argue that the rate at which transitions from \(i\) to \(j\) to \(k\) occur must equal the rate at which transitions from \(k\) to \(j\) to \(i\) occur.
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Get started for freeConsider a Markov chain in steady state. Say that a \(k\) length run of zeroes ends at time \(m\) if $$ X_{m-k-1} \neq 0, \quad X_{m-k}=X_{m-k+1}=\ldots=X_{m-1}=0, X_{m} \neq 0 $$ Show that the probability of this event is \(\pi_{0}\left(P_{0,0}\right)^{k-1}\left(1-P_{0,0}\right)^{2}\), where \(\pi_{0}\) is the limiting probability of state 0 .
Consider the Ehrenfest urn model in which \(M\) molecules are distributed between two urns, and at each time point one of the molecules is chosen at random and is then removed from its urn and placed in the other one. Let \(X_{n}\) denote the number of molecules in urn 1 after the \(n\) th switch and let \(\mu_{n}=E\left[X_{n}\right]\). Show that (a) \(\mu_{n+1}=1+(1-2 / M) \mu_{n}\). (b) Use (a) to prove that $$ \mu_{n}=\frac{M}{2}+\left(\frac{M-2}{M}\right)^{n}\left(E\left[X_{0}\right]-\frac{M}{2}\right) $$
Suppose that on each play of the game a gambler either wins 1 with probability \(p\) or loses 1 with probability \(1-p\). The gambler continues betting until she or he is either up \(n\) or down \(m\). What is the probability that the gambler quits a winner?
Consider a branching process having \(\mu<1\). Show that if \(X_{0}=1\), then the expected number of individuals that ever exist in this population is given by \(1 /(1-\mu)\). What if \(X_{0}=n ?\)
Coin 1 comes up heads with probability \(0.6\) and \(\operatorname{coin} 2\) with probability \(0.5 . \mathrm{A}\) coin is continually flipped until it comes up tails, at which time that coin is put aside and we start flipping the other one. (a) What proportion of flips use coin 1? (b) If we start the process with \(\operatorname{coin} 1\) what is the probability that \(\operatorname{coin} 2\) is used on the fifth flip?
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