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

A prisoner is trapped in a cell containing three doors. The first door leads to a tunnel that returns him to his cell after two days of travel. The second leads to a tunnel that returns him to his cell after three days of travel. The third door leads immediately to freedom. (a) Assuming that the prisoner will always select doors 1,2, and 3 with probabilities \(0.5,0.3,0.2\), what is the expected number of days until he reaches freedom? (b) Assuming that the prisoner is always equally likely to choose among those doors that he has not used, what is the expected number of days until he reaches freedom? (In this version, for instance, if the prisoner initially tries door 1 , then when he returns to the cell, he will now select only from doors 2 and 3.) (c) For parts (a) and (b) find the variance of the number of days until the prisoner reaches freedom.

Short Answer

Expert verified
The expected number of days until the prisoner reaches freedom is 2.125 days for the case with given probabilities, and 1.5 days for the case with equally likely unused doors. The variances for the number of days for these cases are 1.734375 and 1.015625, respectively.

Step by step solution

01

(a) Find expected number of days using given probabilities

Let X be the random variable representing the number of days until the prisoner reaches freedom. We can calculate the expected value of X (E[X]) using the given probabilities: \( E[X] = 0.5*(2 + E[X]) + 0.3*(3 + E[X]) + 0.2*0 \) \( E[X] = E[X] + 1.7 \) Since 0.2E[X] = 0.5*(2 + E[X]) + 0.3*(3 + E[X]), we can find the expected number of days to freedom as: \( E[X] = \frac{1.7}{0.8} = 2.125 \) So, the expected number of days until the prisoner reaches freedom while using the given probabilities is 2.125 days.
02

(b) Find expected number of days with equally likely unused doors

Now, let's calculate the expected number of days with the other strategy that the prisoner chooses only from those doors he hasn't used before: Let E1, E2, and E3 represent the expected values if the prisoner starts from door 1, door 2, and door 3 respectively. Now: \( E1 = \frac{1}{2}(2+E2) + \frac{1}{2}(2+E3) \) \( E2 = \frac{1}{2}(3+E1) + \frac{1}{2}(3+E3) \) \( E3 = 0 \) By solving these equations, we get: \( E1 = \frac{17}{8} = 2.125 \) \( E2 = \frac{19}{8} = 2.375 \) \( E3 = 0 \) Now, the expected value of the days to reach freedom in this case is simply the average of E1, E2, and E3: \( E[X] = \frac{2.125 + 2.375 + 0}{3} = 1.5 \) So, the expected number of days until the prisoner reaches freedom while using the new strategy is 1.5 days.
03

(c) Find the variance for parts (a) and (b)

For each part, we will first calculate the second moment (E[X^2]) and then use it to find the variance, which is given by Var(X) = E[X^2] - (E[X])^2. Part (a) Variance: \( E[X^2] = 0.5*(2 + E[X])^2 + 0.3*(3 + E[X])^2 + 0.2*0 \) Substitute the E[X] we found in part (a): \( E[X^2] = 0.5*(4.125)^2 + 0.3*(5.125)^2 \) \( E[X^2] = 8.515625 \) Now we can find the Variance: \( Var(X) = 8.515625 - (2.125)^2 \) \( Var(X) = 1.734375 \) Part (b) Variance: In this case, we need to find E1^2, E2^2, and E3^2 first: \( E1^2 = \frac{1}{2}(2+E2)^2 + \frac{1}{2}(2+E3)^2 \) \( E2^2 = \frac{1}{2}(3+E1)^2 + \frac{1}{2}(3+E3)^2 \) \( E3^2 = 0 \) Evaluating these and finding E[X]^2: \( E[X]^2 = \frac{202.625}{8} \) \( Var(X) = \frac{202.625}{8} - (1.5)^2 \) \( Var(X) = 1.015625 \) In conclusion: a) The expected number of days using given probabilities: 2.125 days b) The expected number of days with equally likely unused doors: 1.5 days c) The variance of the number of days for parts (a) and (b): 1.734375 and 1.015625, respectively.

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

An urn contains \(n\) balls, with ball \(i\) having weight \(w_{i}, i=1, \ldots, n .\) The balls are withdrawn from the urn one at a time according to the following scheme: When \(S\) is the set of balls that remains, ball \(i, i \in S\), is the next ball withdrawn with probability \(w_{i} / \sum_{j \in S} w_{j} .\) Find the expected number of balls that are withdrawn before ball \(i, i=1, \ldots, n\)

Let \(X_{i}, i \geqslant 1\), be independent uniform \((0,1)\) random variables, and define \(N\) by $$ N=\min \left\\{n: X_{n}

If \(R_{i}\) denotes the random amount that is earned in period \(i\), then \(\sum_{i=1}^{\infty} \beta^{i-1} R_{i}\), where \(0<\beta<1\) is a specified constant, is called the total discounted reward with discount factor \(\beta .\) Let \(T\) be a geometric random variable with parameter \(1-\beta\) that is independent of the \(R_{i} .\) Show that the expected total discounted reward is equal to the expected total (undiscounted) reward earned by time \(T\). That is, show that $$ E\left[\sum_{i=1}^{\infty} \beta^{i-1} R_{i}\right]=E\left[\sum_{i=1}^{T} R_{i}\right] $$

The random variables \(X\) and \(Y\) are said to have a bivariate normal distribution if their joint density function is given by $$ \begin{aligned} f(x, y)=& \frac{1}{2 \pi \sigma_{x} \sigma_{y} \sqrt{1-\rho^{2}}} \exp \left\\{-\frac{1}{2\left(1-\rho^{2}\right)}\right.\\\ &\left.\times\left[\left(\frac{x-\mu_{x}}{\sigma_{x}}\right)^{2}-\frac{2 \rho\left(x-\mu_{x}\right)\left(y-\mu_{y}\right)}{\sigma_{x} \sigma_{y}}+\left(\frac{y-\mu_{y}}{\sigma_{y}}\right)^{2}\right]\right\\} \end{aligned} $$ for \(-\infty0, \sigma_{y}>0,-\infty<\mu_{x}<\infty,-\infty<\mu_{y}<\infty\) (a) Show that \(X\) is normally distributed with mean \(\mu_{x}\) and variance \(\sigma_{x}^{2}\), and \(Y\) is normally distributed with mean \(\mu_{y}\) and variance \(\sigma_{y}^{2}\). (b) Show that the conditional density of \(X\) given that \(Y=y\) is normal with mean \(\mu_{x}+\left(\rho \sigma_{x} / \sigma_{y}\right)\left(y-\mu_{y}\right)\) and variance \(\sigma_{x}^{2}\left(1-\rho^{2}\right)\) The quantity \(\rho\) is called the correlation between \(X\) and \(Y\). It can be shown that $$ \begin{aligned} \rho &=\frac{E\left[\left(X-\mu_{x}\right)\left(Y-\mu_{y}\right)\right]}{\sigma_{x} \sigma_{y}} \\ &=\frac{\operatorname{Cov}(X, Y)}{\sigma_{x} \sigma_{y}} \end{aligned} $$

An individual traveling on the real line is trying to reach the origin. However, the larger the desired step, the greater is the variance in the result of that step. Specifically, whenever the person is at location \(x\), he next moves to a location having mean 0 and variance \(\beta x^{2}\). Let \(X_{n}\) denote the position of the individual after having taken \(n\) steps. Supposing that \(X_{0}=x_{0}\), find (a) \(E\left[X_{n}\right]\); (b) \(\operatorname{Var}\left(X_{n}\right)\)

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