Skip to main content

Math Algebra Thomas' Calculus and Linear Algebra and Its Applications Package for the Georgia Institute of Technology, 1/e

Exercises 27-30 concern the Markov chain model for scoring a tennis match described in Section 10.1, Exercise 31. Suppose that players A and B are playing a tennis match, that the probability that player A wins any point is p = .6, and that the game is currently at “deuce.” 28. Find the probability that player A wins the game. 31. To win a game in tennis, one player must score four points and must also score at least two points more than his or her opponent. Thus if the two players have scored an equal number of points (four or more), which is called “deuce” in tennis jargon, one player must then score two points in a row to win the game. Suppose that players A and B are playing a game of tennis which is at deuce. If A wins the next point it is called “advantage A,” while if B wins the point it is “advantage B.” If the game is at advantage A and player A wins the next point, then player A wins the game. If player B wins the point at advantage A, the game is back at deuce. a. Suppose the probability of player A winning any point is p. Model the progress of a tennis game starting at deuce using a Markov chain with the following five states. 1. deuce 2. advantage A 3. advantage B 4. A wins the game 5. B wins the game Find the transition matrix for this Markov chain. b. Let p = .6. Find the probability that the game will be at “advantage B” after three points starting at deduce.

Exercises 27-30 concern the Markov chain model for scoring a tennis match described in Section 10.1, Exercise 31. Suppose that players A and B are playing a tennis match, that the probability that player A wins any point is p = .6, and that the game is currently at “deuce.” 28. Find the probability that player A wins the game. 31. To win a game in tennis, one player must score four points and must also score at least two points more than his or her opponent. Thus if the two players have scored an equal number of points (four or more), which is called “deuce” in tennis jargon, one player must then score two points in a row to win the game. Suppose that players A and B are playing a game of tennis which is at deuce. If A wins the next point it is called “advantage A,” while if B wins the point it is “advantage B.” If the game is at advantage A and player A wins the next point, then player A wins the game. If player B wins the point at advantage A, the game is back at deuce. a. Suppose the probability of player A winning any point is p. Model the progress of a tennis game starting at deuce using a Markov chain with the following five states. 1. deuce 2. advantage A 3. advantage B 4. A wins the game 5. B wins the game Find the transition matrix for this Markov chain. b. Let p = .6. Find the probability that the game will be at “advantage B” after three points starting at deduce.

Solution Summary: The author explains that the probability that player A wins the game when it is currently at "deduce" is 913.

Thomas' Calculus and Linear Algebra and Its Applications Package for the Georgia Institute of Technology, 1/e

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

See similar textbooks

Videos

Textbook Question

Chapter 10.5, Problem 28E

Exercises 27-30 concern the Markov chain model for scoring a tennis match described in Section 10.1, Exercise 31. Suppose that players A and B are playing a tennis match, that the probability that player A wins any point is p = .6, and that the game is currently at “deuce.”

28. Find the probability that player A wins the game.

31. To win a game in tennis, one player must score four points and must also score at least two points more than his or her opponent. Thus if the two players have scored an equal number of points (four or more), which is called “deuce” in tennis jargon, one player must then score two points in a row to win the game. Suppose that players A and B are playing a game of tennis which is at deuce. If A wins the next point it is called “advantage A,” while if B wins the point it is “advantage B.” If the game is at advantage A and player A wins the next point, then player A wins the game. If player B wins the point at advantage A, the game is back at deuce.

a. Suppose the probability of player A winning any point is p. Model the progress of a tennis game starting at deuce using a Markov chain with the following five states.

1. deuce
2. advantage A
3. advantage B
4. A wins the game
5. B wins the game

Find the transition matrix for this Markov chain.

b. Let p = .6. Find the probability that the game will be at “advantage B” after three points starting at deduce.

Expert Solution & Answer

Want to see the full answer?

Check out a sample textbook solution

Blurred answer

Chapter 10.5, Problem 27E

Chapter 10.5, Problem 29E

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. 2PP Ch. 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. 3E Ch. 10.1 - Prob. 4E Ch. 10.1 - In Exercises 5 and 6, the transition matrix P for...Ch. 10.1 - Prob. 6E Ch. 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. 19E Ch. 10.1 - In Exercises 19 and 20, suppose a mouse wanders...Ch. 10.1 - Prob. 21E Ch. 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. 25E Ch. 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. 29E Ch. 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. 33E Ch. 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. 2E Ch. 10.2 - In Exercises 3 and 4, consider a Markov chain on...Ch. 10.2 - Prob. 4E Ch. 10.2 - Prob. 5E Ch. 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. 8E Ch. 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. 13E Ch. 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. 17E Ch. 10.2 - Prob. 18E Ch. 10.2 - Prob. 19E Ch. 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. 23E Ch. 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. 27E Ch. 10.2 - Consider beginning with an individual of known...Ch. 10.2 - Prob. 29E Ch. 10.2 - Consider the Bernoulli-Laplace diffusion model...Ch. 10.2 - Prob. 31E Ch. 10.2 - Prob. 32E Ch. 10.2 - Prob. 33E Ch. 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. 37E Ch. 10.2 - Consider a simple random walk on a finite...Ch. 10.2 - Prob. 39E Ch. 10.3 - Consider the Markov chain on {1, 2, 3, 4} with...Ch. 10.3 - Prob. 1E Ch. 10.3 - In Exercises 16, consider a Markov chain with...Ch. 10.3 - Prob. 3E Ch. 10.3 - Prob. 4E Ch. 10.3 - Prob. 5E Ch. 10.3 - Prob. 6E Ch. 10.3 - Consider the mouse in the following maze from...Ch. 10.3 - Prob. 8E Ch. 10.3 - Prob. 9E Ch. 10.3 - Prob. 10E Ch. 10.3 - Prob. 11E Ch. 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. 14E Ch. 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. 19E Ch. 10.3 - In Exercises 19 and 20, consider the mouse in the...Ch. 10.3 - Prob. 21E Ch. 10.3 - Prob. 22E Ch. 10.3 - Suppose that the weather in Charlotte is modeled...Ch. 10.3 - Prob. 24E Ch. 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. 27E Ch. 10.3 - Prob. 28E Ch. 10.3 - Prob. 29E Ch. 10.3 - Prob. 30E Ch. 10.3 - Prob. 31E Ch. 10.3 - Prob. 32E Ch. 10.3 - Prob. 33E Ch. 10.3 - In Exercises 33 and 34, consider the Markov chain...Ch. 10.3 - Prob. 35E Ch. 10.3 - Prob. 36E Ch. 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. 14E Ch. 10.4 - Prob. 15E Ch. 10.4 - Prob. 16E Ch. 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. 24E Ch. 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. 27E Ch. 10.4 - Prob. 28E Ch. 10.4 - Prob. 29E Ch. 10.5 - Prob. 1PP Ch. 10.5 - Consider a Markov chain on {1, 2, 3, 4} with...Ch. 10.5 - Prob. 1E Ch. 10.5 - Prob. 2E Ch. 10.5 - In Exercises 13, find the fundamental matrix of...Ch. 10.5 - Prob. 4E Ch. 10.5 - Prob. 5E Ch. 10.5 - Prob. 6E Ch. 10.5 - Prob. 7E Ch. 10.5 - Prob. 8E Ch. 10.5 - Prob. 9E Ch. 10.5 - Prob. 10E Ch. 10.5 - Prob. 11E Ch. 10.5 - Prob. 12E Ch. 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. 15E Ch. 10.5 - Prob. 16E Ch. 10.5 - Prob. 17E Ch. 10.5 - Prob. 18E Ch. 10.5 - Prob. 19E Ch. 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. 22E Ch. 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. 34E Ch. 10.5 - Prob. 35E Ch. 10.5 - Prob. 36E Ch. 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. 39E Ch. 10.6 - Let A be the matrix just before Example 1. Explain...Ch. 10.6 - Prob. 2PP Ch. 10.6 - Prob. 1E Ch. 10.6 - Prob. 2E Ch. 10.6 - Prob. 3E Ch. 10.6 - Prob. 4E Ch. 10.6 - Prob. 5E Ch. 10.6 - Prob. 6E Ch. 10.6 - Major League batting statistics for the 2006...Ch. 10.6 - Prob. 8E Ch. 10.6 - Prob. 9E Ch. 10.6 - Prob. 10E Ch. 10.6 - Prob. 11E Ch. 10.6 - Prob. 12E Ch. 10.6 - Prob. 14E Ch. 10.6 - Prob. 15E Ch. 10.6 - Prob. 16E Ch. 10.6 - Prob. 17E Ch. 10.6 - In the previous exercise, let p be the probability...

Knowledge Booster

Background pattern image

$Algebra$

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.
Consumer Preference In a population of 100,000 consumers, there are 20,000 users of Brand A, 30,000 users of Brand B, and 50,000 who use neither brand. During any month, a Brand A user has a 20 probability of switching to Brand B and a 5 of not using either brand. A Brand B user has a 15 probability of switching to Brand A and a 10 probability of not using either brand. A nonuser has a 10 probability of purchasing Brand A and a 15 probability of purchasing Brand B. How many people will be in each group a in 1 month, b in 2 months, and c in 18 months?

Recommended textbooks for you

Elementary Linear Algebra (MindTap Course List)
Algebra
ISBN:9781305658004
Author:Ron Larson
Publisher:Cengage Learning
Algebra & Trigonometry with Analytic Geometry
Algebra
ISBN:9781133382119
Author:Swokowski
Publisher:Cengage

Text book image

Elementary Linear Algebra (MindTap Course List)

Algebra

ISBN:9781305658004

Author:Ron Larson

Publisher:Cengage Learning

Text book image

Algebra & Trigonometry with Analytic Geometry

Algebra

ISBN:9781133382119

Author:Swokowski

Publisher:Cengage

SEE MORE TEXTBOOKS

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