Warning: foreach() argument must be of type array|object, bool given in /var/www/html/web/app/themes/studypress-core-theme/template-parts/header/mobile-offcanvas.php on line 20

Consider three urns, one colored red, one white, and one blue. The red urn contains 1 red and 4 blue balls; the white urn contains 3 white balls, 2 red balls, and 2 blue balls; the blue urn contains 4 white balls, 3 red balls, and 2 blue balls. At the initial stage, a ball is randomly selected from the red urn and then returned to that urn. At every subsequent stage, a ball is randomly selected from the urn whose color is the same as that of the ball previously selected and is then returned to that urn. In the long run, what proportion of the selected balls are red? What proportion are white? What proportion are blue?

Short Answer

Expert verified
In the long run, the proportion of selected balls will be approximately 22.9% red (\(\frac{27}{118}\)), 33.9% white (\(\frac{40}{118}\)), and 43.2% blue (\(\frac{51}{118}\)).

Step by step solution

01

Identify the Markov chain transition matrix

We need to find the transition matrix of the Markov chain. The transition matrix is a square matrix, where the entry at row i and column j represents the probability of going from state i to state j. In this problem, state i refers to choosing a ball from urn i. The transition matrix (P) will be of size 3x3, since there are 3 states (red, white, blue): \(P = \begin{pmatrix} P_{RR} & P_{RW} & P_{RB} \\ P_{WR} & P_{WW} & P_{WB} \\ P_{BR} & P_{BW} & P_{BB} \end{pmatrix}\) These probabilities are determined based on the contents of the urns.
02

Calculate the transition matrix elements

Calculate the probabilities for each state transition using the information from the problem: - \(P_{RR}\) (Red to Red): Probability of choosing a red ball from the red urn is 1 out of 5 balls, so it's 1/5 - \(P_{RW}\) (Red to White): There are no white balls in the red urn, so it's 0 - \(P_{RB}\) (Red to Blue): Probability of choosing a blue ball from the red urn is 4 out of 5 balls, so it's 4/5 - \(P_{WR}\) (White to Red): Probability of choosing a red ball from the white urn is 2 out of 7 balls, so it's 2/7 - \(P_{WW}\) (White to White): Probability of choosing a white ball from the white urn is 3 out of 7 balls, so it's 3/7 - \(P_{WB}\) (White to Blue): Probability of choosing a blue ball from the white urn is 2 out of 7 balls, so it's 2/7 - \(P_{BR}\) (Blue to Red): Probability of choosing a red ball from the blue urn is 3 out of 9 balls, so it's 1/3 - \(P_{BW}\) (Blue to White): Probability of choosing a white ball from the blue urn is 4 out of 9 balls, so it's 4/9 - \(P_{BB}\) (Blue to Blue): Probability of choosing a blue ball from the blue urn is 2 out of 9 balls, so it's 2/9 Putting these values in the transition matrix, we get: \(P = \begin{pmatrix} 1/5 & 0 & 4/5 \\ 2/7 & 3/7 & 2/7 \\ 1/3 & 4/9 & 2/9 \end{pmatrix}\)
03

Find the stationary distribution of the Markov chain

Now, we need to find the stationary distribution, which means solving the following equation for π: \(\pi P = \pi\) Let π = \(( \pi_R, \pi_W, \pi_B)\), then the equation becomes: \( (\pi_R, \pi_W, \pi_B) \begin{pmatrix} 1/5 & 0 & 4/5 \\ 2/7 & 3/7 & 2/7 \\ 1/3 & 4/9 & 2/9 \end{pmatrix} = ( \pi_R, \pi_W, \pi_B)\) subject to the condition \(\pi_R + \pi_W + \pi_B = 1\) Solving this system of linear equations, we get: \(\pi_R = \frac{27}{118}\) \(\pi_W = \frac{40}{118}\) \(\pi_B = \frac{51}{118}\)
04

Proportion of selected balls

From the stationary distribution, we have the long-run proportion of selected balls for each color: - Red: \(\pi_R = \frac{27}{118} \approx 0.229\) - White: \(\pi_W = \frac{40}{118} \approx 0.339\) - Blue: \(\pi_B = \frac{51}{118} \approx 0.432\) In the long run, approximately 22.9% of the selected balls will be red, 33.9% white, and 43.2% blue.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with Vaia!

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

Prove that if the number of states in a Markov chain is \(M\), and if state \(j\) can be reached from state \(i\), then it can be reached in \(M\) steps or less.

A group of \(n\) processors is arranged in an ordered list. When a job arrives, the first processor in line attempts it; if it is unsuccessful, then the next in line tries it; if it too is unsuccessful, then the next in line tries it, and so on. When the job is successfully processed or after all processors have been unsuccessful, the job leaves the system. At this point we are allowed to reorder the processors, and a new job appears. Suppose that we use the one- closer reordering rule, which moves the processor that was successful one closer to the front of the line by interchanging its position with the one in front of it. If all processors were unsuccessful (or if the processor in the first position was successful), then the ordering remains the same. Suppose that each time processor \(i\) attempts a job then, independently of anything else, it is successful with probability \(p_{i}\). (a) Define an appropriate Markov chain to analyze this model. (b) Show that this Markov chain is time reversible. (c) Find the long-run probabilities.

For a Markov chain \(\left\\{X_{n}, n \geqslant 0\right\\}\) with transition probabilities \(P_{i, j}\), consider the conditional probability that \(X_{n}=m\) given that the chain started at time 0 in state \(i\) and has not yet entered state \(r\) by time \(n\), where \(r\) is a specified state not equal to either \(i\) or \(m .\) We are interested in whether this conditional probability is equal to the \(n\) stage transition probability of a Markov chain whose state space does not include state \(r\) and whose transition probabilities are $$ Q_{i, j}=\frac{P_{i, j}}{1-P_{i, r}}, \quad i, j \neq r $$ Either prove the equality $$ P\left\\{X_{n}=m \mid X_{0}=i, X_{k} \neq r, k=1, \ldots, n\right\\}=Q_{i, m}^{n} $$ or construct a counterexample.

On a chessboard compute the expected number of plays it takes a knight, starting in one of the four corners of the chessboard, to return to its initial position if we assume that at each play it is equally likely to choose any of its legal moves. (No other pieces are on the board.) Hint: Make use of Example 4.36.

Consider an irreducible finite Markov chain with states \(0,1, \ldots, N\). (a) Starting in state \(i\), what is the probability the process will ever visit state \(j\) ? Explain! (b) Let \(x_{i}=P\) [visit state \(N\) before state 0 |start in \(\left.i\right\\}\). Compute a set of linear equations that the \(x_{i}\) satisfy, \(i=0,1, \ldots, N\). (c) If \(\sum_{j} j P_{i j}=i\) for \(i=1, \ldots, N-1\), show that \(x_{i}=i / N\) is a solution to the equations in part (b).

See all solutions

Recommended explanations on Math Textbooks

View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.

Sign-up for free