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For the Markov chain with states \(1,2,3,4\) whose transition probability matrix \(\mathbf{P}\) is as specified below find \(f_{i 3}\) and \(s_{i 3}\) for \(i=1,2,3\). $$ \mathbf{P}=\left[\begin{array}{llll} 0.4 & 0.2 & 0.1 & 0.3 \\ 0.1 & 0.5 & 0.2 & 0.2 \\ 0.3 & 0.4 & 0.2 & 0.1 \\ 0 & 0 & 0 & 1 \end{array}\right] $$

Short Answer

Expert verified
In summary, to find the first passage probabilities \(f_{i3}\) and mean first passage times \(s_{i3}\) for \(i=1,2,3\) with given transition probability matrix \(\mathbf{P}\), we utilize the recursive formula for first passage probabilities and the definition of mean first passage time. Calculation involves iterating over the recursions until a desired level of precision is obtained.

Step by step solution

01

Calculate the first passage probabilities, \(f_{i3}\)

The first passage probabilities \(f_{ij}\) are defined as the probability that the Markov chain will first visit state \(j\) from state \(i\). They can be calculated recursively using the following formula: $$ f_{ij}^{(n)} = \sum_{k \neq j} p_{ik} f_{kj}^{(n-1)}, \quad n \geq 1, $$ where \(f_{ij}^{(n)}\) denotes the probability that the process first enters state \(j\) at step \(n\) and \(p_{ik}\) is the transition probability from state \(i\) to state \(k\). For \(i=1,2,3\), we will compute \(f_{i3}\) using the formula above: 1. When \(i = 1\), \(f_{13}^{(n)} = 0.1\cdot f_{33}^{(n-1)}+0.2\cdot f_{23}^{(n-1)}+0.4\cdot f_{13}^{(n-1)}\). 2. When \(i = 2\), \(f_{23}^{(n)} = 0.2\cdot f_{33}^{(n-1)}+0.5\cdot f_{23}^{(n-1)}+0.1\cdot f_{13}^{(n-1)}\). 3. When \(i = 3\), \(f_{33}^{(n)} = 0.2\cdot f_{33}^{(n-1)}+0.4\cdot f_{23}^{(n-1)}+0.3\cdot f_{13}^{(n-1)}\). With these recursions, we can calculate the first passage probabilities until the desired convergence is obtained.
02

Calculate the mean first passage times, \(s_{i3}\)

The mean first passage time, \(s_{ij}\), is the expected time it takes for the Markov chain to reach state \(j\) for the first time from state \(i\). It can be calculated using the first passage probabilities, \(f_{ij}^{(n)}\), as follows: $$ s_{ij} = \sum_{n=1}^{\infty} n \cdot f_{ij}^{(n)}. $$ Using the previously calculated first passage probabilities, \(f_{ij}^{(n)}\), for \(i=1,2,3\), we can now compute the mean first passage times, \(s_{i3}\), by summing the series for each \(i\). Remember that, in general, we must use numerical methods to compute both first passage probabilities and mean first passage times. By iterating over the recursions, we can obtain these values to a desired level of precision.

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Most popular questions from this chapter

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 \(X_{0}=n ?\)

Consider a population of individuals each of whom possesses two genes that can be either type \(A\) or type \(a\). Suppose that in outward appearance type \(A\) is dominant and type \(a\) is recessive. (That is, an individual will have only the outward characteristics of the recessive gene if its pair is aa.) Suppose that the population has stabilized, and the percentages of individuals having respective gene pairs \(A A, a a\), and \(A a\) are \(p, q\), and \(r .\) Call an individual dominant or recessive depending on the outward characteristics it exhibits. Let \(S_{11}\) denote the probability that an offspring of two dominant parents will be recessive; and let \(S_{10}\) denote the probability that the offspring of one dominant and one recessive parent will be recessive. Compute \(S_{11}\) and \(S_{10}\) to show that \(S_{11}=S_{10}^{2} .\) (The quantities \(S_{10}\) and \(S_{11}\) are known in the genetics literature as Snyder's ratios.)

A total of \(m\) white and \(m\) black balls are distributed among two urns, with each urn containing \(m\) balls. At each stage, a ball is randomly selected from each urn and the two selected balls are interchanged. Let \(X_{n}\) denote the number of black balls in urn 1 after the \(n\) th interchange. (a) Give the transition probabilities of the Markov chain \(X_{n}, n \geqslant 0\). (b) Without any computations, what do you think are the limiting probabilities of this chain? (c) Find the limiting probabilities and show that the stationary chain is time reversible.

Consider 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 .

Let \(Y_{n}\) be the sum of \(n\) independent rolls of a fair die. Find \(\lim _{n \rightarrow \infty} P\left\\{Y_{n}\right.\) is a multiple of 13\(\\}\) Hint: Define an appropriate Markov chain and apply the results of Exercise \(20 .\)

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