Thomas' Calculus and Linear Algebra and Its Applications Package for the Georgia Institute of Technology, 1/e
5th Edition
ISBN: 9781323132098
Author: Thomas, Lay
Publisher: PEARSON C
expand_more
expand_more
format_list_bulleted
Textbook Question
Chapter 10.1, Problem 27E
Consider a model for signal transmission in which data is sen as two-bit bytes. Then there are four possible bytes, 00, 01, 10, and 11, which are the states of the Markov chain. At each stage there is a probability p that each bit will pass through the stage unchanged.
- a. Construct the transition matrix for the model.
- b. Suppose that p = .99. Find the probability that the signal “01” will still be “01” after a three-stage transmission.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solutionStudents have asked these similar questions
Suppose that Phone A has a 20% market share initially, with Phone B
having an 80% market share. The transition matrix to the right
represents customers who currently own phones A and B and the
probability that they will upgrade to a new phone A or B. Use this
transition matrix to find the share for each phone after the following
upgrades.
A B
A
0.8
0.2
B
0.25 0.75
(a) First upgrade
(b) Second upgrade
(c) Third upgrade
(a) With the first upgrade, Phone A has % of the market and Phone B has
% of the market.
(Round your answers to two decimal places as needed.)
In any given day the air quality in a certain city is either good or bad. Records show that when the air quality is good on one day, then there is a 95% chance that it will be good the next day, and when the air quality is bad on one day, then there is 45% chance it will be bad the next day.
a. Give the transition matrix.
b. if the air quality is good today, what is the probability it will be good two days from now?
c. if the air quality is bad today, what is the probability it will be bad three days from now?
d. if the there is 20% chance the air quality is good today, what is the probability it will be good tomorrow?
A system consists of five components, each can be operational or not. Each day one operational component is used and it will fail with probability 20%. Any time there no operational components at the end of a day, maintenance will be performed and all non-operational components will be repaired (with probability 1). The system does not perform any other tasks on the day of repairs.
Model the system as a Markov chain
Write down equations for determining long-run proportions.
Suppose that you are interested in the average number of days that the system is under repair. Explain how you would find it using your model.
Chapter 10 Solutions
Thomas' Calculus and Linear Algebra and Its Applications Package for the Georgia Institute of Technology, 1/e
Ch. 10.1 - Fill in the missing entries in the stochastic...Ch. 10.1 - Prob. 2PPCh. 10.1 - In Exercises 1 and 2, determine whether P is a...Ch. 10.1 - In Exercises 1 and 2, determine whether P is a...Ch. 10.1 - Prob. 3ECh. 10.1 - Prob. 4ECh. 10.1 - In Exercises 5 and 6, the transition matrix P for...Ch. 10.1 - Prob. 6ECh. 10.1 - In Exercises 7 and 8, the transition matrix P for...Ch. 10.1 - In Exercises 7 and 8, the transition matrix P for...
Ch. 10.1 - Consider a pair of Ehrenfest urns labeled A and B....Ch. 10.1 - Consider a pair of Ehrenfest urns labeled A and B....Ch. 10.1 - Consider an unbiased random walk on the set...Ch. 10.1 - Consider a biased random walk on the set {1,2,3,4}...Ch. 10.1 - In Exercises 13 and 14, find the transition matrix...Ch. 10.1 - In Exercises 13 and 14, find the transition matrix...Ch. 10.1 - In Exercises 15 and 16, find the transition matrix...Ch. 10.1 - In Exercises 15 and 16, find the transition matrix...Ch. 10.1 - The mouse is placed in room 2 of the maze shown...Ch. 10.1 - The mouse is placed in room 3 of the maze shown...Ch. 10.1 - Prob. 19ECh. 10.1 - In Exercises 19 and 20, suppose a mouse wanders...Ch. 10.1 - Prob. 21ECh. 10.1 - In Exercises 21 and 22, mark each statement True...Ch. 10.1 - The weather in Charlotte, North Carolina, can be...Ch. 10.1 - Suppose that whether it rains in Charlotte...Ch. 10.1 - Prob. 25ECh. 10.1 - Consider a set of five webpages hyperlinked by the...Ch. 10.1 - Consider a model for signal transmission in which...Ch. 10.1 - Consider a model for signal transmission in which...Ch. 10.1 - Prob. 29ECh. 10.1 - Another model for diffusion is called the...Ch. 10.1 - To win a game in tennis, one player must score...Ch. 10.1 - Volleyball uses two different scoring systems in...Ch. 10.1 - Prob. 33ECh. 10.2 - Consider the Markov chain on {1, 2, 3} with...Ch. 10.2 - In Exercises 1 and 2, consider a Markov chain on...Ch. 10.2 - Prob. 2ECh. 10.2 - In Exercises 3 and 4, consider a Markov chain on...Ch. 10.2 - Prob. 4ECh. 10.2 - Prob. 5ECh. 10.2 - In Exercises 5 and 6, find the matrix to which Pn...Ch. 10.2 - In Exercises 7 and 8, determine whether the given...Ch. 10.2 - Prob. 8ECh. 10.2 - Consider a pair of Ehrenfest urns with a total of...Ch. 10.2 - Consider a pair of Ehrenfest urns with a total of...Ch. 10.2 - Consider an unbiased random walk with reflecting...Ch. 10.2 - Consider a biased random walk with reflecting...Ch. 10.2 - Prob. 13ECh. 10.2 - In Exercises 13 and 14, consider a simple random...Ch. 10.2 - In Exercises 15 and 16, consider a simple random...Ch. 10.2 - In Exercises 15 and 16, consider a simple random...Ch. 10.2 - Prob. 17ECh. 10.2 - Prob. 18ECh. 10.2 - Prob. 19ECh. 10.2 - Consider the mouse in the following maze, which...Ch. 10.2 - In Exercises 21 and 22, mark each statement True...Ch. 10.2 - In Exercises 21 and 22, mark each statement True...Ch. 10.2 - Prob. 23ECh. 10.2 - Suppose that the weather in Charlotte is modeled...Ch. 10.2 - In Exercises 25 and 26, consider a set of webpages...Ch. 10.2 - In Exercises 25 and 26, consider a set of webpages...Ch. 10.2 - Prob. 27ECh. 10.2 - Consider beginning with an individual of known...Ch. 10.2 - Prob. 29ECh. 10.2 - Consider the Bernoulli-Laplace diffusion model...Ch. 10.2 - Prob. 31ECh. 10.2 - Prob. 32ECh. 10.2 - Prob. 33ECh. 10.2 - Let 0 p, q 1, and define P = [p1q1pq] a. Show...Ch. 10.2 - Let 0 p, q 1, and define P = [pq1pqq1pqp1pqpq]...Ch. 10.2 - Let A be an m m stochastic matrix, let x be in m...Ch. 10.2 - Prob. 37ECh. 10.2 - Consider a simple random walk on a finite...Ch. 10.2 - Prob. 39ECh. 10.3 - Consider the Markov chain on {1, 2, 3, 4} with...Ch. 10.3 - Prob. 1ECh. 10.3 - In Exercises 16, consider a Markov chain with...Ch. 10.3 - Prob. 3ECh. 10.3 - Prob. 4ECh. 10.3 - Prob. 5ECh. 10.3 - Prob. 6ECh. 10.3 - Consider the mouse in the following maze from...Ch. 10.3 - Prob. 8ECh. 10.3 - Prob. 9ECh. 10.3 - Prob. 10ECh. 10.3 - Prob. 11ECh. 10.3 - Consider an unbiased random walk with absorbing...Ch. 10.3 - In Exercises 13 and 14, consider a simple random...Ch. 10.3 - Prob. 14ECh. 10.3 - In Exercises 15 and 16, consider a simple random...Ch. 10.3 - In Exercises 15 and 16, consider a simple random...Ch. 10.3 - Consider the mouse in the following maze from...Ch. 10.3 - Consider the mouse in the following maze from...Ch. 10.3 - Prob. 19ECh. 10.3 - In Exercises 19 and 20, consider the mouse in the...Ch. 10.3 - Prob. 21ECh. 10.3 - Prob. 22ECh. 10.3 - Suppose that the weather in Charlotte is modeled...Ch. 10.3 - Prob. 24ECh. 10.3 - The following set of webpages hyperlinked by the...Ch. 10.3 - The following set of webpages hyperlinked by the...Ch. 10.3 - Prob. 27ECh. 10.3 - Prob. 28ECh. 10.3 - Prob. 29ECh. 10.3 - Prob. 30ECh. 10.3 - Prob. 31ECh. 10.3 - Prob. 32ECh. 10.3 - Prob. 33ECh. 10.3 - In Exercises 33 and 34, consider the Markov chain...Ch. 10.3 - Prob. 35ECh. 10.3 - Prob. 36ECh. 10.4 - Consider the Markov chain on {1, 2, 3, 4} with...Ch. 10.4 - In Exercises 1-6, consider a Markov chain with...Ch. 10.4 - In Exercises 1-6, consider a Markov chain with...Ch. 10.4 - In Exercises 1-6, consider a Markov chain with...Ch. 10.4 - In Exercises 1-6, consider a Markov chain with...Ch. 10.4 - In Exercises 1-6, consider a Markov chain with...Ch. 10.4 - In Exercises 1-6, consider a Markov chain with...Ch. 10.4 - In Exercises 7-10, consider a simple random walk...Ch. 10.4 - In Exercises 7-10, consider a simple random walk...Ch. 10.4 - In Exercises 7-10, consider a simple random walk...Ch. 10.4 - In Exercises 7-10: consider a simple random walk...Ch. 10.4 - Reorder the states in the Markov chain in Exercise...Ch. 10.4 - Reorder the states in the Markov chain in Exercise...Ch. 10.4 - Reorder the states in the Markov chain in Exercise...Ch. 10.4 - Prob. 14ECh. 10.4 - Prob. 15ECh. 10.4 - Prob. 16ECh. 10.4 - Find the transition matrix for the Markov chain in...Ch. 10.4 - Find the transition matrix for the Markov chain in...Ch. 10.4 - Consider the mouse in the following maze from...Ch. 10.4 - Consider the mouse in the following maze from...Ch. 10.4 - In Exercises 21-22, mark each statement True or...Ch. 10.4 - In Exercises 21-22, mark each statement True or...Ch. 10.4 - Confirm Theorem 5 for the Markov chain in Exercise...Ch. 10.4 - Prob. 24ECh. 10.4 - Consider the Markov chain on {1, 2, 3} with...Ch. 10.4 - Follow the plan of Exercise 25 to confirm Theorem...Ch. 10.4 - Prob. 27ECh. 10.4 - Prob. 28ECh. 10.4 - Prob. 29ECh. 10.5 - Prob. 1PPCh. 10.5 - Consider a Markov chain on {1, 2, 3, 4} with...Ch. 10.5 - Prob. 1ECh. 10.5 - Prob. 2ECh. 10.5 - In Exercises 13, find the fundamental matrix of...Ch. 10.5 - Prob. 4ECh. 10.5 - Prob. 5ECh. 10.5 - Prob. 6ECh. 10.5 - Prob. 7ECh. 10.5 - Prob. 8ECh. 10.5 - Prob. 9ECh. 10.5 - Prob. 10ECh. 10.5 - Prob. 11ECh. 10.5 - Prob. 12ECh. 10.5 - Consider a simple random walk on the following...Ch. 10.5 - Consider a simple random walk on the following...Ch. 10.5 - Prob. 15ECh. 10.5 - Prob. 16ECh. 10.5 - Prob. 17ECh. 10.5 - Prob. 18ECh. 10.5 - Prob. 19ECh. 10.5 - Consider the mouse in the following maze from...Ch. 10.5 - In Exercises 21 and 22, mark each statement True...Ch. 10.5 - Prob. 22ECh. 10.5 - Suppose that the weather in Charlotte is modeled...Ch. 10.5 - Suppose that the weather in Charlotte is modeled...Ch. 10.5 - Consider a set of webpages hyperlinked by the...Ch. 10.5 - Consider a set of webpages hyperlinked by the...Ch. 10.5 - Exercises 27-30 concern the Markov chain model for...Ch. 10.5 - Exercises 27-30 concern the Markov chain model for...Ch. 10.5 - Exercises 27-30 concern the Markov chain model for...Ch. 10.5 - Exercises 27-30 concern the Markov chain model for...Ch. 10.5 - Exercises 31-36 concern the two Markov chain...Ch. 10.5 - Exercises 31-36 concern the two Markov chain...Ch. 10.5 - Exercises 31-36 concern the two Markov chain...Ch. 10.5 - Prob. 34ECh. 10.5 - Prob. 35ECh. 10.5 - Prob. 36ECh. 10.5 - Consider a Markov chain on {1, 2, 3, 4, 5, 6} with...Ch. 10.5 - Consider a Markov chain on {1,2,3,4,5,6} with...Ch. 10.5 - Prob. 39ECh. 10.6 - Let A be the matrix just before Example 1. Explain...Ch. 10.6 - Prob. 2PPCh. 10.6 - Prob. 1ECh. 10.6 - Prob. 2ECh. 10.6 - Prob. 3ECh. 10.6 - Prob. 4ECh. 10.6 - Prob. 5ECh. 10.6 - Prob. 6ECh. 10.6 - Major League batting statistics for the 2006...Ch. 10.6 - Prob. 8ECh. 10.6 - Prob. 9ECh. 10.6 - Prob. 10ECh. 10.6 - Prob. 11ECh. 10.6 - Prob. 12ECh. 10.6 - Prob. 14ECh. 10.6 - Prob. 15ECh. 10.6 - Prob. 16ECh. 10.6 - Prob. 17ECh. 10.6 - In the previous exercise, let p be the probability...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, algebra and related others by exploring similar questions and additional content below.Similar questions
- Explain how you can determine the steady state matrix X of an absorbing Markov chain by inspection.arrow_forwardSuppose you have the following transition probabilities. P = Product A B C A 0.40 0 0.60 B 0.30 0.35 0.35 C 0 0.50 0.50 a. Calculate the 3-step transition matrix and interpret each elements.arrow_forwardEach item is inspected and is declared to either pass or fail. The machine can work in automatic or manual mode. If it outputs two failed items in a row in automatic mode, it is switched to manual. Once it produces two passing items in a row in manual mode, it is switched back to automatic. Sup- pose that failure rate is a in automatic and b in manual. You modeled the system as a Markov chain with a diagram given below, where states represent the mode and the status of the previously man- ufactured item, so for example, state “manual-1 success” represents that the machine is in manual mode and the previous item passed.arrow_forward
- Can someone please help me with this question. I am having so much trouble.arrow_forwardA Markov chain model for a species has four states: State 0 (Lower Risk), State 1 (Vulnerable), State 2 (Threatened), and State 3 (Extinct). For t 2 0, you are given that: 01 zit = 0.03 12 t= 0.05 23 Hit = 0.06 This species is currently in state 0. Calculate the probability this species will be in state 2 ten years later. Assume that reentry is not possible. (Note: This question is similar to #46.2 but with constant forces of mortality) Possīble Answers A 0.02 0.03 0.04 D 0.05 E 0.06arrow_forwardplease show answers and explain steps for how to solvearrow_forward
- At Community College, 10% of all business majors switched to another major the next semester, while the remaining 90% continued as business majors. Of all nonbusiness majors, 20% switched to a business major the following semester, while the rest did not. Set up these data as a Markov transition matrix, and calculate the probability that a business major will no longer be a business major in the long runarrow_forwardAileen, a Scottish spy, has three fake identities that she uses to get information. The process is really quite involved, but she uses a Markov chain process to make it more difficult for her Irish adversaries to track her: "Hope" (state 1), "Trixie"(state 2), and "Fiona" (state 3). The transition matrix is .5 .5 P = .1 .9 .6 .4 On the first observation, Aileen used "Fiona" as her identity. Which of the three fake identities is she most likely to have used on the third observation? O Fiona and Trixie are equally likely Fiona O Hope and Fiona are equally likely None of the others are correct Hope Trixiearrow_forwardA factory worker will quit with probability 1/2 during her first month, with probability 1/4 during her second month and with probability 1/8 after that. Whenever someone quits, their replacement will start at the beginning of the next month. Model the status of each position as a Markov chain with 3 states. Identify the states and transition matrix. Write down the system of equations determining the long-run proportions. Suppose there are 900 workers in the factory. Find the average number of the workers who have been there for more than 2 months.arrow_forward
- At Suburban Community College, 30% of all business majors switched to another major the next semester, while the remaining 70% continued as business majors. Of all non-business majors, 10% switched to a business major the following semester, while the rest did not. Set up these data as a Markov transition matrix. HINT [See Example 1.] (Let 1 business majors, and 2 = non-business majors.) = Calculate the probability that a business major will no longer be a business major in two semesters' time.arrow_forwardAt Suburban Community College, 40% of all business majors switched to another major the next semester, while the remaining 60% continued as business majors. Of all non-business majors, 20% switched to a business major the following semester, while the rest did not. Set up these data as a Markov transition matrix. (Let 1 = business majors, and 2 = non-business majors.) calculate the probability that a business major will no longer be a business major in two semesters' time.arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
- Elementary Linear Algebra (MindTap Course List)AlgebraISBN:9781305658004Author:Ron LarsonPublisher:Cengage Learning
Elementary Linear Algebra (MindTap Course List)
Algebra
ISBN:9781305658004
Author:Ron Larson
Publisher:Cengage Learning
Finite Math: Markov Chain Example - The Gambler's Ruin; Author: Brandon Foltz;https://www.youtube.com/watch?v=afIhgiHVnj0;License: Standard YouTube License, CC-BY
Introduction: MARKOV PROCESS And MARKOV CHAINS // Short Lecture // Linear Algebra; Author: AfterMath;https://www.youtube.com/watch?v=qK-PUTuUSpw;License: Standard Youtube License
Stochastic process and Markov Chain Model | Transition Probability Matrix (TPM); Author: Dr. Harish Garg;https://www.youtube.com/watch?v=sb4jo4P4ZLI;License: Standard YouTube License, CC-BY