Solutions Manual for the book Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang c Chapman & Hall/CRC Press, 2015

Transcription

1 Solutions Manual for the book Introduction to Probability by Joseph K. Blitzstein and Jessica Hwang c Chapman & Hall/CRC Press, 2015 Joseph K. Blitzstein and Jessica Hwang Departments of Statistics, Harvard University and Stanford University September 7, 2017

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3 Contents 1 Probability and counting 1 2 Conditional probability 29 3 Random variables and their distributions 71 4 Expectation 93 5 Continuous random variables Moments Joint distributions Transformations Conditional expectation Inequalities and limit theorems Markov chains Markov chain Monte Carlo Poisson processes 333 iii

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5 1 Probability and counting Counting 1. How many ways are there to permute the letters in the word MISSISSIPPI? The word has 4 S s, 4 I s, 2 P s, and 1 M. Let s choose where to put the S s, then where to put the I s, then where to put the P s, and then the location of the M is determined. By the multiplication rule, there are ( 11 4 ( 7 4 ( 3 2 = possibilities. Alternatively, we can start with 11! and adjust for overcounting: 11! 4!4!2! = (a How many 7-digit phone numbers are possible, assuming that the first digit can t be a 0 or a 1? (b Re-solve (a, except now assume also that the phone number is not allowed to start with 911 (since this is reserved for emergency use, and it would not be desirable for the system to wait to see whether more digits were going to be dialed after someone has dialed 911. (a By the multiplication rule, there are possibilities. (b There are 10 4 phone numbers in (a that start with 911 (again by the multiplication rule, since the first 3 digits are 911 and the remaining 4 digits are unconstrained. Excluding these and using the result of (a, the number of possibilities is = Fred is planning to go out to dinner each night of a certain week, Monday through Friday, with each dinner being at one of his ten favorite restaurants. (a How many possibilities are there for Fred s schedule of dinners for that Monday through Friday, if Fred is not willing to eat at the same restaurant more than once? (b How many possibilities are there for Fred s schedule of dinners for that Monday through Friday, if Fred is willing to eat at the same restaurant more than once, but is not willing to eat at the same place twice in a row (or more? (a By the multiplication rule, there are = possibilities. (b By the multiplication rule, there are = possibilities, since Monday s dinner can be at any of the 10 restaurants, and for Tuesday through Friday, each dinner can be at any of the 10 restaurants except the one where Fred ate on the previous night. 1

6 2 4. A round-robin tournament is being held with n tennis players; this means that every player will play against every other player exactly once. (a How many possible outcomes are there for the tournament (the outcome lists out who won and who lost for each game? (b How many games are played in total? (a For each of the ( n 2 unordered pairs of players, there is 1 game, so there are 2 ( n 2 possible outcomes for the tournament. (b There are ( n 2 games played, as noted in (a. 5. A knock-out tournament is being held with 2 n tennis players. This means that for each round, the winners move on to the next round and the losers are eliminated, until only one person remains. For example, if initially there are 2 4 = 16 players, then there are 8 games in the first round, then the 8 winners move on to round 2, then the 4 winners move on to round 3, then the 2 winners move on to round 4, the winner of which is declared the winner of the tournament. (There are various systems for determining who plays whom within a round, but these do not matter for this problem. (a How many rounds are there? (b Count how many games in total are played, by adding up the numbers of games played in each round. (c Count how many games in total are played, this time by directly thinking about it without doing almost any calculation. Hint: How many players need to be eliminated? (a There are n rounds, since each round cuts the number of remaining players in half. (b There are 2 n /2 = 2 n 1 games in the first round, then 2 n 2 games in the second round, and so on, until there are only 2 players left in the final round. Using the formula for the sum of a finite geometric series (see the math appendix, the total number of games is n 1 = 1 2n 1 2 = 2n 1. (c A much easier way to see that the number of games is 2 n 1 is to note that each game eliminates one player, and 2 n 1 players need to be eliminated to leave one winner. 6. There are 20 people at a chess club on a certain day. They each find opponents and start playing. How many possibilities are there for how they are matched up, assuming that in each game it does matter who has the white pieces (in a chess game, one player has the white pieces and the other player has the black pieces? 20! There are ways to determine who plays whom without considering color, ! by the multiplication rule or the result of Example (Partnerships. For each game, there are 2 choices for who has the white pieces, so overall the number of possibilities is ! ! = 20! 10! = Alternatively, imagine a long table with 10 chessboards in a row, with 10 chairs on each

7 Probability and counting 3 side of the table (for the chess players to sit in. Chess pieces are set up at each board, oriented so that the white pieces are all on one side of the table and the black pieces are all on the other side of the table. There are 20! ways for the chess players to be assigned to chairs, and once the players are seated in their chairs, a configuration for the chess games has been determined. This procedure overcounts by a factor of 10! since, after matching up the players, we can permute the players on one side of the table in any way, as long as we also permute the players on the other side of the table in the same way. So again the number of possibilities is 20! 10!. 7. Two chess players, A and B, are going to play 7 games. Each game has three possible outcomes: a win for A (which is a loss for B, a draw (tie, and a loss for A (which is a win for B. A win is worth 1 point, a draw is worth 0.5 points, and a loss is worth 0 points. (a How many possible outcomes for the individual games are there, such that overall player A ends up with 3 wins, 2 draws, and 2 losses? (b How many possible outcomes for the individual games are there, such that A ends up with 4 points and B ends up with 3 points? (c Now assume that they are playing a best-of-7 match, where the match will end when either player has 4 points or when 7 games have been played, whichever is first. For example, if after 6 games the score is 4 to 2 in favor of A, then A wins the match and they don t play a 7th game. How many possible outcomes for the individual games are there, such that the match lasts for 7 games and A wins by a score of 4 to 3? (a Writing W for win, D for draw, and L for loss (for player A, an outcome of the desired form is any permutation of WWWDDLL. So there are possible outcomes of the desired form. 7! 3!2!2! = 210 (b To end up with 4 points, A needs to have one of the following results: (i 4 wins and 3 losses; (ii 3 wins, 2 draws, and 2 losses; (iii 2 wins, 4 draws, and 1 loss; or (iv 1 win and 6 draws. Reasoning as in (a and adding up these possibilities, there are possible outcomes of the desired form. 7! 4!3! + 7! 3!2!2! + 7! 2!4!1! + 7! 1!6! = 357 (c For the desired outcomes, either (i player A is ahead 3.5 to 2.5 after 6 games and then draws game 7, or (ii the match is tied (3 to 3 after 6 games and then player A wins game 7. Reasoning as in (b, there are possibilities of type (i and possibilities of type (ii, so overall there are possible outcomes of the desired form. 6! 3!1!2! + 6! 2!3!1! + 6! 1!5! = 126 6! 3!3! + 6! 2!2!2! + 6! 1!4!1! + 1 = = 267

8 4 8. s (a How many ways are there to split a dozen people into 3 teams, where one team has 2 people, and the other two teams have 5 people each? (b How many ways are there to split a dozen people into 3 teams, where each team has 4 people? (a Pick any 2 of the 12 people to make the 2 person team, and then any 5 of the remaining 10 for the first team of 5, and then the remaining 5 are on the other team of 5; this overcounts by a factor of 2 though, since there is no designated first team of 5. So the number of possibilities is ( ( /2 = Alternatively, politely ask the 12 people to line up, and then let the first 2 be the team of 2, the next 5 be a team of 5, and then last 5 be a team of 5. There are 12! ways for them to line up, but it does not matter which order they line up in within each group, nor does the order of the 2 teams 12! of 5 matter, so the number of possibilities is = !5!5! 2 12! (b By either of the approaches above, there are ways to divide the people into a 4!4!4! Team A, a Team B, and a Team C, if we care about which team is which (this is called a multinomial coefficient. Since here it doesn t matter which team is which, this over 12! counts by a factor of 3!, so the number of possibilities is = !4!4!3! 9. s (a How many paths are there from the point (0, 0 to the point (110, 111 in the plane such that each step either consists of going one unit up or one unit to the right? (b How many paths are there from (0, 0 to (210, 211, where each step consists of going one unit up or one unit to the right, and the path has to go through (110, 111? (a Encode a path as a sequence of U s and R s, like URURURUUUR... UR, where U and R stand for up and right respectively. The sequence must consist of 110 R s and 111 U s, and to determine the sequence we just need to specify where the R s are located. So there are ( possible paths. (b There are ( paths to (110, 111, as above. From there, we need 100 R s and 100 U s to ( get to (210, 211, so by the multiplication rule the number of possible paths is 200 ( To fulfill the requirements for a certain degree, a student can choose to take any 7 out of a list of 20 courses, with the constraint that at least 1 of the 7 courses must be a statistics course. Suppose that 5 of the 20 courses are statistics courses. (a How many choices are there for which 7 courses to take? (b Explain intuitively why the answer to (a is not ( ( (a There are ( ( 20 7 ways to choose 7 courses if there are no constraints, but 15 7 of these have no statistics courses. So there are ( 20 7 ( 15 7 = sets of 7 courses that contain at least one statistics course. (b An incorrect argument would be there are ( 5 1 to choose a statistics course (let s knock that requirement out of the way, then we can choose any other 6 courses and then ( 19 6 choices for the remaining 6 courses. This is incorrect since it s possible (and

9 Probability and counting 5 often a good idea! to take more than one statistics course. A possibility containing, for example, the statistics courses Stat 110 and Stat 111 together with 5 non-statistics courses would be counted twice in ( ( , once with Stat 110 as the choice for the ( 5 1 term and once with Stat 111 as the choice. So it makes sense that the true answer is much less than ( ( Let A and B be sets with A = n, B = m. (a How many functions are there from A to B (i.e., functions with domain A, assigning an element of B to each element of A? (b How many one-to-one functions are there from A to B (see Section A.2.1 of the math appendix for information about one-to-one functions? (a By the multiplication rule, there are m n functions f from A to B, since for each a A there are m possible ways to define f(a. (b Now values can t be repeated, so there are m (m 1 (m 2 (m n + 1 possibilities for n m, and there are no possibilities for n > m. 12. Four players, named A, B, C, and D, are playing a card game. A standard, well-shuffled deck of cards is dealt to the players (so each player receives a 13-card hand. (a How many possibilities are there for the hand that player A will get? (Within a hand, the order in which cards were received doesn t matter. (b How many possibilities are there overall for what hands everyone will get, assuming that it matters which player gets which hand, but not the order of cards within a hand? (c Explain intuitively why the answer to Part (b is not the fourth power of the answer to Part (a. (a There are ( possibilities since player A gets 13 out of 52 cards, without replacement and with order not mattering. (b There are ( ( possibilities for A s hand. For each of these, there are possibilities for B s hand. For each of these, there are ( possibilities for C s hand. After 3 hands have been determined, the 4th is also determined. So the number of possibilities is ( ( ( = 52! (13! The expression with factorials could have been obtained directly by imagining shuffling the cards and giving the first 13 to A, the next 13 to B etc., and then adjusting for the fact that the order of cards within each player s hand doesn t matter. As Wikipedia remarks (at as of December 1, 2014, The immenseness of this number can be understood by answering the question How large an area would you need to spread all possible bridge deals if each deal would occupy only one square millimeter?. The answer is: an area more than a hundred million times the total area of Earth. (c The answer to (b, though an extremely large number, is much smaller than the fourth power of the answer to (a since the cards are dealt without replacement. This makes the number of possibilities ( ( ( 26 ( 13 ( rather than 52 ( 52 ( 52 (

10 6 13. A certain casino uses 10 standard decks of cards mixed together into one big deck, which we will call a superdeck. Thus, the superdeck has = 520 cards, with 10 copies of each card. How many different 10-card hands can be dealt from the superdeck? The order of the cards does not matter, nor does it matter which of the original 10 decks the cards came from. Express your answer as a binomial coefficient. Hint: Bose-Einstein. A hand is determined by specifying how many times each of the 52 different cards occurs. Number the 52 cards in a standard deck as 1, 2,..., 52, and let x i be the number of times card i occurs in a hand (e.g., 3 for the Ace of Spades in the above example. Then the x i are nonnegative integers with x 1 + x x 52 = 10. By Bose-Einstein, the number of solutions is ( ( = You are ordering two pizzas. A pizza can be small, medium, large, or extra large, with any combination of 8 possible toppings (getting no toppings is allowed, as is getting all 8. How many possibilities are there for your two pizzas? For one pizza, there are = 2 10 possibilities. For two pizzas, there are ( 2 10 ways to choose two distinct kinds of pizza, and 2 10 possibilities with two copies of 2 the same kind of pizza. So the number of possibilities is ( = Alternatively, think of choosing 2 pizza types with replacement, where order doesn t matter. Then Bose-Einstein gives the same answer: the number of possibilities is ( = Story proofs 15. s Give a story proof that n k=0 ( n k = 2 n. Consider picking a subset of n people. There are ( n k choices with size k, on the one hand, and on the other hand there are 2 n subsets by the multiplication rule. 16. s Show that for all positive integers n and k with n k, ( ( ( n n n =, k k 1 k doing this in two ways: (a algebraically and (b with a story, giving an interpretation for why both sides count the same thing. Hint for the story proof: Imagine n + 1 people, with one of them pre-designated as president.

11 Probability and counting 7 (a For the algebraic proof, start with the definition of the binomial coefficients in the left-hand side, and do some algebraic manipulation as follows: ( ( n n n! + = k k 1 k!(n k! + n! (k 1!(n k + 1! = = = (n k + 1n! + (kn! k!(n k + 1! n!(n + 1 k!(n k + 1! ( n + 1 k. (b For the story proof, consider n + 1 people, with one of them pre-designated as president. The right-hand side is the number of ways to choose k out of these n + 1 people, with order not mattering. The left-hand side counts the same thing in a different way, by considering two cases: the president is or isn t in the chosen group. The number of groups of size k which include the president is ( n k 1, since once we fix the president as a member of the group, we only need to choose another k 1 members out of the remaining n people. Similarly, there are ( n k groups of size k that don t include the president. Thus, the two sides of the equation are equal. 17. Give a story proof that for all positive integers n. ( 2 n n 2n 1 k = n(, k n 1 k=1 Hint: Consider choosing a committee of size n from two groups of size n each, where only one of the two groups has people eligible to become president. Imagine that there are n juniors and n seniors in a certain club. A committee of size n is chosen, and one of these people becomes president. Suppose though that the president must be a senior. Letting k be the number of seniors on the committee, there are ( n k ways to choose the seniors, ( ( n n k = n k ways to choose the juniors, and after these choices are made there are k choices of president. So the overall number of possibilities is the left-hand side of the identity. Alternatively, we can choose the president first (as any of the n seniors, and then choose any n 1 of the remaining 2n 1 people to form the rest of the committee. This gives the right-hand side of the identity. 18. s (a Show using a story proof that ( ( ( ( ( k k + 1 k + 2 n n =, k k k k k + 1 where n and k are positive integers with n k. This is called the hockey stick identity. Hint: Imagine arranging a group of people by age, and then think about the oldest person in a chosen subgroup. (b Suppose that a large pack of Haribo gummi bears can have anywhere between 30 and 50 gummi bears. There are 5 delicious flavors: pineapple (clear, raspberry (red, orange (orange, strawberry (green, mysteriously, and lemon (yellow. There are 0 nondelicious flavors. How many possibilities are there for the composition of such a pack of

12 8 gummi bears? You can leave your answer in terms of a couple binomial coefficients, but not a sum of lots of binomial coefficients. (a Consider choosing k + 1 people out of a group of n + 1 people. Call the oldest person in the subgroup Aemon. If Aemon is also the oldest person in the full group, then there are ( n k choices for the rest of the subgroup. If Aemon is the second oldest in the full group, then there are ( n 1 k choices since the oldest person in the full group can t be chosen. In general, if there are j people in the full group who are younger than Aemon, then there are ( j k possible choices for the rest of the subgroup. Thus, ( ( n j=k j k = n + 1 k + 1. (b For a pack of i gummi bears, there are ( ( 5+i 1 i = i+4 ( i = i+4 4 possibilities since the situation is equivalent to getting a sample of size i from the n = 5 flavors (with replacement, and with order not mattering. So the total number of possibilities is j=34 50 i=30 ( i + 4 = 4 54 j=34 ( j. 4 Applying the previous part, we can simplify this by writing ( ( ( ( 54 j 54 j 33 j 55 = = j=4 (This works out to possibilities! j=4 ( Define { n k} as the number of ways to partition {1, 2,..., n} into k nonempty subsets, or the number of ways to have n students split up into k groups such that each group has at least one student. For example, { 4 2} = 7 because we have the following possibilities: {1}, {2, 3, 4} {2}, {1, 3, 4} {3}, {1, 2, 4} {4}, {1, 2, 3} {1, 2}, {3, 4} {1, 3}, {2, 4} {1, 4}, {2, 3} Prove the following identities: (a { } { } { } n + 1 n n = + k. k k 1 k Hint: I m either in a group by myself or I m not. (b ( { } { } n n j n + 1 =. j k k + 1 j=k Hint: First decide how many people are not going to be in my group. (a The left-hand side is the number of ways to divide n + 1 people into k nonempty groups. Now let s count this a different way. Say I m the (n + 1st person. Either I m in a group by myself or I m not. If I m in a group by myself, then there are { n k 1} ways

13 Probability and counting 9 to divide the remaining n people into k 1 nonempty groups. Otherwise, the n people other than me form k nonempty groups, which can be done in { n k} ways, and then I can join any of those k groups. So in total, there are { { n k 1} + k n k} possibilities, which is the right-hand side. (b The right-hand side is the number of ways to divide n+1 people into k+1 nonempty groups. Say I m the (n + 1st person. Let j be the number of people not in my group. Then ( k j n. The number of possible divisions with j people not in my group is n { j } ( j k since we have n j possibilities for which j specific people are not in my group (and then it s determined who is in my group and then { j k} possibilities for how to divide those j people into k groups that are not my group. Summing over all possible j gives the left-hand side. Note: The quantities { n k} are known as Stirling numbers of the second kind. 20. The Dutch mathematician R.J. Stroeker remarked: Every beginning student of number theory surely must have marveled at the miraculous fact that for each natural number n the sum of the first n positive consecutive cubes is a perfect square. [29] Furthermore, it is the square of the sum of the first n positive integers! That is, n 3 = ( n 2. Usually this identity is proven by induction, but that does not give much insight into why the result is true, nor does it help much if we wanted to compute the left-hand side but didn t already know this result. In this problem, you will give a story proof of the identity. (a Give a story proof of the identity n = ( n Hint: Consider a round-robin tournament (see Exercise 4. (b Give a story proof of the identity ( ( ( n 3 n + 1 n + 1 n + 1 = It is then just basic algebra (not required for this problem to check that the square of the right-hand side in (a is the right-hand side in (b. Hint: Imagine choosing a number between 1 and n and then choosing 3 numbers between 0 and n smaller than the original number, with replacement. Then consider cases based on how many distinct numbers were chosen. (a Consider a chess tournament with n + 1 players, where everyone plays against everyone else once. A total of ( n+1 2 games are played. Label the players 0, 1,..., n. Player 0 plays n games, player 1 plays n 1 games not already accounted for, player 2 plays n 2 games not already accounted for, etc. So n + (n 1 + (n = ( n (b Following the hint, let us count the number of choices of (i, j, k, l where i is greater

14 10 than j, k, l. Given i, there are i 3 choices for (j, k, l, which gives the left-hand side. On the other ( hand, consider 3 cases: there could be 2, 3, or 4 distinct numbers chosen. There are n+1 2 ways to choose 2 distinct numbers from 0, 1,..., n, giving, e.g., (3, 1, 1, 1. There are ( n+1 4 ways to choose 4 distinct numbers, giving, e.g., (5, 2, 1, 4, but the (2, 1, 4 could be permuted in any order so we multiply by 6. There are ( n+1 3 ways to choose 3 distinct numbers, giving, e.g., (4, 2, 2, 1, but the 2, 2, 1 can be in any order and could have been 1, 1, 2 in any order also, again giving a factor of 6. Adding these cases gives the right-hand side. Naive definition of probability 21. Three people get into an empty elevator at the first floor of a building that has 10 floors. Each presses the button for their desired floor (unless one of the others has already pressed that button. Assume that they are equally likely to want to go to floors 2 through 10 (independently of each other. What is the probability that the buttons for 3 consecutive floors are pressed? The number of possible outcomes for who is going to which floor is 9 3. There are 7 possibilities for which buttons are pressed such that there are 3 consecutive floors: (2, 3, 4, (3, 4, 5,..., (8, 9, 10. For each of these 7 possibilities, there are 3! ways to choose who is going to which floor. So by the naive definition, the probability is 3! = = s A certain family has 6 children, consisting of 3 boys and 3 girls. Assuming that all birth orders are equally likely, what is the probability that the 3 eldest children are the 3 girls? Label the girls as 1, 2, 3 and the boys as 4, 5, 6. Think of the birth order is a permutation of 1, 2, 3, 4, 5, 6, e.g., we can interpret as meaning that child 3 was born first, then child 1, etc. The number of possible permutations of the birth orders is 6!. Now we need to count how many of these have all of 1, 2, 3 appear before all of 4, 5, 6. This means that the sequence must be a permutation of 1, 2, 3 followed by a permutation of 4, 5, 6. So with all birth orders equally likely, we have P (the 3 girls are the 3 eldest children = (3!2 6! = Alternatively, we can use the fact that there are ( 6 3 ways to choose where the girls appear in the birth order (without taking into account the ordering of the girls amongst themselves. These are all equally likely. Of these possibilities, there is only 1 where the 1 3 girls are the 3 eldest children. So again the probability is ( 6 3 = s A city with 6 districts has 6 robberies in a particular week. Assume the robberies are located randomly, with all possibilities for which robbery occurred where equally likely. What is the probability that some district had more than 1 robbery? There are 6 6 possible configurations for which robbery occurred where. There are 6! configurations where each district had exactly 1 of the 6, so the probability of the complement of the desired event is 6!/6 6. So the probability of some district having more than 1 robbery is 1 6!/ Note that this also says that if a fair die is rolled 6 times, there s over a 98% chance that some value is repeated!

15 Probability and counting A survey is being conducted in a city with 1 million residents. It would be far too expensive to survey all of the residents, so a random sample of size 1000 is chosen (in practice, there are many challenges with sampling, such as obtaining a complete list of everyone in the city, and dealing with people who refuse to participate. The survey is conducted by choosing people one at a time, with replacement and with equal probabilities. (a Explain how sampling with vs. without replacement here relates to the birthday problem. (b Find the probability that at least one person will get chosen more than once. (a In the survey problem, people are sampled one by one, and each person randomly is any of the 10 6 residents in the city; in the birthday problem, people show up at a party one by one, and each person randomly has any of 365 possible birthdays. The fact that the same person can be chosen more than once when sampling with replacement is analogous to the fact that more than one person can have the same birthday. (b This problem is isomorphic to (has the same structure as the birthday problem. By the naive definition of probability, the probability of no match is ( 10 6 (10 6 1( ( = 1 1 ( 1 2 ( ( The probability of at least one person being chosen more than once is ( ( 1 2 ( A hash table is a commonly used data structure in computer science, allowing for fast information retrieval. For example, suppose we want to store some people s phone numbers. Assume that no two of the people have the same name. For each name x, a hash function h is used, letting h(x be the location that will be used to store x s phone number. After such a table has been computed, to look up x s phone number one just recomputes h(x and then looks up what is stored in that location. The hash function h is deterministic, since we don t want to get different results every time we compute h(x. But h is often chosen to be pseudorandom. For this problem, assume that true randomness is used. Let there be k people, with each person s phone number stored in a random location (with equal probabilities for each location, independently of where the other people s numbers are stored, represented by an integer between 1 and n. Find the probability that at least one location has more than one phone number stored there. This problem has the same structure as the birthday problem. For k > n, the probability is 1 since then there are more people than locations. For k n, the probability is 1 n(n 1... (n k + 1 n k. 26. s A college has 10 (non-overlapping time slots for its courses, and blithely assigns courses to time slots randomly and independently. A student randomly chooses 3 of the courses to enroll in. What is the probability that there is a conflict in the student s schedule? The probability of no conflict is = So the probability of there being at least one scheduling conflict is 0.28.

16 s For each part, decide whether the blank should be filled in with =, <, or >, and give a clear explanation. (a (probability that the total after rolling 4 fair dice is 21 total after rolling 4 fair dice is 22 (b (probability that a random 2-letter word is a palindrome 1 random 3-letter word is a palindrome (probability that the (probability that a (a >. All ordered outcomes are equally likely here. So for example with two dice, obtaining a total of 9 is more likely than obtaining a total of 10 since there are two ways to get a 5 and a 4, and only one way to get two 5 s. To get a 21, the outcome must be a permutation of (6, 6, 6, 3 (4 possibilities, (6, 5, 5, 5 (4 possibilities, or (6, 6, 5, 4 (4!/2 = 12 possibilities. To get a 22, the outcome must be a permutation of (6, 6, 6, 4 (4 possibilities, or (6, 6, 5, 5 (4!/2 2 = 6 possibilities. So getting a 21 is more likely; in fact, it is exactly twice as likely as getting a 22. (b =. The probabilities are equal, since for both 2-letter and 3-letter words, being a palindrome means that the first and last letter are the same. 28. With definitions as in the previous problem, find the probability that a random n-letter word is a palindrome for n = 7 and for n = 8. The probability of a random 7-letter word being a palindrome is = , since the first 4 letters can be chosen arbitrarily and then the last 3 letters are determined. Similarly, the probability for a random 8-letter word is = s Elk dwell in a certain forest. There are N elk, of which a simple random sample of size n are captured and tagged ( simple random sample means that all ( N n sets of n elk are equally likely. The captured elk are returned to the population, and then a new sample is drawn, this time with size m. This is an important method that is widely used in ecology, known as capture-recapture. What is the probability that exactly k of the m elk in the new sample were previously tagged? (Assume that an elk that was captured before doesn t become more or less likely to be captured again. We can use the naive definition here since we re assuming all samples of size m are equally likely. To have exactly k be tagged elk, we need to choose k of the n tagged elk, and then m k from the N n untagged elk. So the probability is ( n ( k N n m k ( N m, for k such that 0 k n and 0 m k N n, and the probability is 0 for all other values of k (for example, if k > n the probability is 0 since then there aren t even k tagged elk in the entire population!. This is known as a Hypergeometric probability; we will encounter it again in Chapter 3. 1 A palindrome is an expression such as A man, a plan, a canal: Panama that reads the same backwards as forwards (ignoring spaces, capitalization, and punctuation. Assume for this problem that all words of the specified length are equally likely, that there are no spaces or punctuation, and that the alphabet consists of the lowercase letters a,b,...,z.

17 Probability and counting Four cards are face down on a table. You are told that two are red and two are black, and you need to guess which two are red and which two are black. You do this by pointing to the two cards you re guessing are red (and then implicitly you re guessing that the other two are black. Assume that all configurations are equally likely, and that you do not have psychic powers. Find the probability that exactly j of your guesses are correct, for j = 0, 1, 2, 3, 4. There are ( 4 2 = 6 possibilities for where the two red cards are, all equally likely. So there is a 1/6 chance that you will pick both locations of red cards correctly (in which case you also get the locations of the black cards right. And there is a 1/6 chance that both locations you choose actually contain black cards (in which case none of your guesses are correct. This leaves a 4/6 chance that the locations you picked as having red cards consist of 1 red card and 1 black card (in which case the other 2 locations also consist of 1 red card and 1 black card. Thus, for j = 0, 1, 2, 3, 4, the desired probabilities are 1/6, 0, 2/3, 0, 1/6, respectively. 31. s A jar contains r red balls and g green balls, where r and g are fixed positive integers. A ball is drawn from the jar randomly (with all possibilities equally likely, and then a second ball is drawn randomly. (a Explain intuitively why the probability of the second ball being green is the same as the probability of the first ball being green. (b Define notation for the sample space of the problem, and use this to compute the probabilities from (a and show that they are the same. (c Suppose that there are 16 balls in total, and that the probability that the two balls are the same color is the same as the probability that they are different colors. What are r and g (list all possibilities? (a This is true by symmetry. The first ball is equally likely to be any of the g + r balls, so the probability of it being green is g/(g + r. But the second ball is also equally likely to be any of the g + r balls (there aren t certain balls that enjoy being chosen second and others that have an aversion to being chosen second; once we know whether the first ball is green we have information that affects our uncertainty about the second ball, but before we have this information, the second ball is equally likely to be any of the balls. Alternatively, intuitively it shouldn t matter if we pick one ball at a time, or take one ball with the left hand and one with the right hand at the same time. By symmetry, the probabilities for the ball drawn with the left hand should be the same as those for the ball drawn with the right hand. (b Label the balls as 1, 2,..., g + r, such that 1, 2,..., g are green and g + 1,..., g + r are red. The sample space can be taken to be the set of all pairs (a, b with a, b {1,..., g + r} and a b (there are other possible ways to define the sample space, but it is important to specify all possible outcomes using clear notation, and it make sense to be as richly detailed as possible in the specification of possible outcomes, to avoid losing information. Each of these pairs is equally likely, so we can use the naive definition of probability. Let G i be the event that the ith ball drawn is green. The denominator is (g+r(g+r 1 by the multiplication rule. For G 1, the numerator is g(g + r 1, again by the multiplication rule. For G 2, the numerator is also g(g + r 1, since in counting favorable cases, there are g possibilities for the second ball, and for each of those there are g + r 1 favorable possibilities for the first ball (note that the multiplication rule doesn t require the experiments to be listed in chronological order!;

18 14 alternatively, there are g(g 1 + rg = g(g + r 1 favorable possibilities for the second ball being green, as seen by considering 2 cases (first ball green and first ball red. Thus, P (G i = for i {1, 2}, which concurs with (a. g(g + r 1 (g + r(g + r 1 = g g + r, (c Let A be the event of getting one ball of each color. In set notation, we can write A = (G 1 G c 2 (G c 1 G 2. We are given that P (A = P (A c, so P (A = 1/2. Then giving the quadratic equation i.e., P (A = 2gr (g + r(g + r 1 = 1 2, g 2 + r 2 2gr g r = 0, (g r 2 = g + r. But g + r = 16, so g r is 4 or 4. Thus, either g = 10, r = 6, or g = 6, r = s A random 5-card poker hand is dealt from a standard deck of cards. Find the probability of each of the following possibilities (in terms of binomial coefficients. (a A flush (all 5 cards being of the same suit; do not count a royal flush, which is a flush with an ace, king, queen, jack, and 10. (b Two pair (e.g., two 3 s, two 7 s, and an ace. (a A flush can occur in any of the 4 suits (imagine the tree, and for concreteness suppose the suit is Hearts; there are ( 13 5 ways to choose the cards in that suit, except for one way to have a royal flush in that suit. So the probability is 4 (( ( (b Choose the two ranks of the pairs, which specific cards to have for those 4 cards, and then choose the extraneous card (which can be any of the 52 8 cards not of the two chosen ranks. This gives that the probability of getting two pairs is ( 13 2 ( ( A random 13-card hand is dealt from a standard deck of cards. What is the probability that the hand contains at least 3 cards of every suit? The only to have at least 3 cards of every suit is to have exactly 4 cards from one suit and exactly 3 cards from each of the other suits. Choosing which suit the hand has 4 of, and then the specific cards from each suit, the probability is 4 (13 ( ( 52 13

19 Probability and counting A group of 30 dice are thrown. What is the probability that 5 of each of the values 1, 2, 3, 4, 5, 6 appear? To get 5 dice for each of the 6 values, we can choose the 5 out of 30 dice that will be 1 s, then the 5 out of the remaining 25 that will be 2 s, etc. This gives ( ( ( ( ( ( = 30! (5! 6 possibilities; this is known as a multinomial coefficient, sometimes denoted as ( 30 5,5,5,5,5. The right-hand side can also be obtained directly: it is the number of permutations of the sequence By the naive definition, the probability is 30! (5! A deck of cards is shuffled well. The cards are dealt one by one, until the first time an ace appears. (a Find the probability that no kings, queens, or jacks appear before the first ace. (b Find the probability that exactly one king, exactly one queen, and exactly one jack appear (in any order before the first ace. (a The 2 s through 10 s are irrelevant, so we can assume the deck consists of aces, kings, queens, and jacks. The event of interest is that the first card is an ace. This has probability 1/4 since the first card is equally likely to be any card. (b Continue as in (a. The probability that the deck starts as KQJA is ! 16! = The KQJ could be in any order, so the desired probability is 3! = Alternatively, note that there are possibilities for the first 4 cards, of which are favorable. So by the naive definition, the probability is Tyrion, Cersei, and ten other people are sitting at a round table, with their seating arrangement having been randomly assigned. What is the probability that Tyrion and Cersei are sitting next to each other? Find this in two ways: (a using a sample space of size 12!, where an outcome is fully detailed about the seating; (b using a much smaller sample space, which focuses on Tyrion and Cersei. (a Label the seats in clockwise order as 1, 2,..., 12, starting from some fixed seat. Give the people other than Tyrion and Cersei ID numbers 1, 2,..., 10. The outcome is (t, c, s 1,..., s 10, where t is Tyrion s seat assignment, c is Cersei s, and s j is person j s.

20 16 To count the number of ways in which Tyrion and Cersei can be seated together, let Tyrion sit anywhere (12 possibilities, Cersei sit either to Tyrion s left or to his right (2 possibilities, and let everyone else fill the remaining 10 seats in any way (10! possibilities. By the multiplication rule and the naive definition, the probability is ! 12! = ! ! = (b Now let s just consider the ( 12 2 possible seat assignments of Tyrion and Cersei, not worrying about which of these 2 seats goes to Tyrion or the details of where the other 10 people will sit. There are 12 assignments in which they sit together (without caring about order: {1, 2}, {2, 3},..., {11, 12}, {12, 1}. So the probability is in agreement with (a. 12 = 2 11, ( An organization with 2n people consists of n married couples. A committee of size k is selected, with all possibilities equally likely. Find the probability that there are exactly j married couples within the committee. The probability is 0 if j > n (not enough couples or k 2j < 0 (not enough space on the committee, so assume 0 j n and k 2j 0. There are ( 2n k possible compositions of the committee. There are ( n j ways to choose which j married couples are on the committee. Once they are chosen, there are ( n j k 2j ways to choose which of the other married couples are represented on the committee. For each of those k 2j couples, we then need to choose which person within the couple will be on the committee. Overall, the probability is ( n j 2 k 2j ( n j k 2j ( 2n k. 38. There are n balls in a jar, labeled with the numbers 1, 2,..., n. A total of k balls are drawn, one by one with replacement, to obtain a sequence of numbers. (a What is the probability that the sequence obtained is strictly increasing? (b What is the probability that the sequence obtained is increasing? (Note: In this book, increasing means nondecreasing. (a There is a one-to-one correspondence between strictly increasing sequences a 1 < < a k and subsets {a 1,..., a k } of size k, so the probability is ( n k /n k. (b There is a one-to-one correspondence between increasing sequences of length k and ways of choosing k balls with replacement, so the probability is ( n+k 1 k /n k. 39. Each of n balls is independently placed into one of n boxes, with all boxes equally likely. What is the probability that exactly one box is empty? In order to have exactly one empty box, there must be one empty box, one box with two balls, and n 2 boxes with one ball (if two or more boxes each had at least two balls, then there would not be enough balls left to avoid having more than one empty box. Choose which box is empty, then which has two balls, then assign balls to the boxes with one ball, and then it is determined which balls are in the box with two balls. This gives that the probability is n(n 1n(n 1(n n n = n!(n 1 2 n n 1.

21 Probability and counting s A norepeatword is a sequence of at least one (and possibly all of the usual 26 letters a,b,c,...,z, with repetitions not allowed. For example, course is a norepeatword, but statistics is not. Order matters, e.g., course is not the same as source. A norepeatword is chosen randomly, with all norepeatwords equally likely. Show that the probability that it uses all 26 letters is very close to 1/e. The number of norepeatwords having all 26 letters is the number of ordered arrangements of 26 letters: 26!. To construct a norepeatword with k 26 letters, we first select k letters from the alphabet ( ( 26 k selections and then arrange them into a word (k! arrangements. Hence there are ( 26 k k! norepeatwords with k letters, with k ranging from 1 to 26. With all norepeatwords equally likely, we have P (norepeatword having all 26 letters = = = # norepeatwords having all 26 letters # norepeatwords 26! 26! = k! 26 k=1 ( 26 k 26 k= ! 24! 1! 26! k!(26 k! k! The denominator is the first 26 terms in the Taylor series e x = 1 + x + x 2 /2! +..., evaluated at x = 1. Thus the probability is approximately 1/e (this is an extremely good approximation since the series for e converges very quickly; the approximation for e differs from the truth by less than Axioms of probability 41. Show that for any events A and B, P (A + P (B 1 P (A B P (A B P (A + P (B. For each of these three inequalities, give a simple criterion for when the inequality is actually an equality (e.g., give a simple condition such that P (A B = P (A B if and only if the condition holds. By Theorem 1.6.2, we have P (A B P (A B since A B A B. Using the fact that P (A B = P (A + P (B P (A B and the fact that probability is always between 0 and 1, we have P (A B = P (A + P (B P (A B P (A + P (B 1 and P (A B P (A + P (B. Now let us investigate when equality occurs for the above inequalities. Writing P (A B = P (A B + P (A B c + P (A c B, we have that P (A B = P (A B if and only if P (A B c and P (A c B are both 0. (This says that there is no probability mass in A but not in B or vice versa. The main example where this will hold is when A and B are the same event. Looking at the proof of P (A + P (B 1 P (A B, we see that equality will hold if and only if P (A B = 1. Looking at the proof of P (A + P (B 1 P (A B, we see that equality will hold if and only if P (A B = Let A and B be events. The difference B A is defined to be the set of all elements of B that are not in A. Show that if A B, then P (B A = P (B P (A,

22 18 directly using the axioms of probability. Let A B. The events A and B A are disjoint and their union is B, so as desired. P (A + P (B A = P (A (B A = P (B, 43. Let A and B be events. The symmetric difference A B is defined to be the set of all elements that are in A or B but not both. In logic and engineering, this event is also called the XOR (exclusive or of A and B. Show that directly using the axioms of probability. We have P (A B = P (A + P (B 2P (A B, P (A B = P (A B c + P (A c B, since A B is the union of the disjoint events A B c and P (A c B. Similarly, we have Adding the above two equations gives Thus, P (A = P (A B c + P (A B, P (B = P (B A c + P (B A. P (A + P (B = P (A B c + P (A c B + 2P (A B. P (A B = P (A B c + P (A c B = P (A + P (B 2P (A B. 44. Let A 1, A 2,..., A n be events. Let B k be the event that exactly k of the A i occur, and C k be the event that at least k of the A i occur, for 0 k n. Find a simple expression for P (B k in terms of P (C k and P (C k+1. Saying that at least k of the A i occur amounts to saying that either exactly k of the A i occur or at least k + 1 occur. These are disjoint possibilities. So P (C k = P (B k + P (C k+1, which gives P (B k = P (C k P (C k Events A and B are independent if P (A B = P (AP (B (independence is explored in detail in the next chapter. (a Give an example of independent events A and B in a finite sample space S (with neither equal to or S, and illustrate it with a Pebble World diagram. (b Consider the experiment of picking a random point in the rectangle R = {(x, y : 0 < x < 1, 0 < y < 1}, where the probability of the point being in any particular region contained within R is the area of that region. Let A 1 and B 1 be rectangles contained within R, with areas not equal to 0 or 1. Let A be the event that the random point is in A 1, and B be the event that the random point is in B 1. Give a geometric description of when it is true that A and B are independent. Also, give an example where they are independent and another example where they are not independent.

23 Probability and counting 19 (c Show that if A and B are independent, then P (A B = P (A + P (B P (AP (B = 1 P (A c P (B c. (a Consider a sample space S consisting of 4 pebbles, each with probability 1/4. Let A consist of two of the pebbles and B consist of two of the pebbles, with A B consisting of a single pebble, as illustrated below. A B Then A and B are independent since P (A B = 1/4 = P (AP (B. (b Geometrically, independence of A and B says that the area of the intersection of A 1 and B 1 is the product of the areas of A 1 and B 1. An example where independence does not hold is when A 1 and B 1 are disjoint (non-overlapping. An example where independence holds is when A 1 is the left half of R and B 1 is the lower half. A more general example where independence holds is when A 1 = {(x, y R : x a} and B 1 = {(x, y R : y b}, where a and b are constants in (0, 1. (c Let A and B be independent. Then P (A B = P (A + P (B P (A B = P (A + P (B P (AP (B. Factoring out P (A from the terms containing it and later doing likewise with P (B c, we can also write P (A B as P (A(1 P (B + P (B = (1 P (A c P (B c + 1 P (B c = 1 P (A c P (B c. 46. s Arby has a belief system assigning a number P Arby (A between 0 and 1 to every event A (for some sample space. This represents Arby s degree of belief about how likely A is to occur. For any event A, Arby is willing to pay a price of 1000 P Arby (A dollars to buy a certificate such as the one shown below: Certificate The owner of this certificate can redeem it for $1000 if A occurs. No value if A does not occur, except as required by federal, state, or local law. No expiration date. Likewise, Arby is willing to sell such a certificate at the same price. Indeed, Arby is willing to buy or sell any number of certificates at this price, as Arby considers it the fair price. Arby stubbornly refuses to accept the axioms of probability. In particular, suppose that there are two disjoint events A and B with P Arby (A B P Arby (A + P Arby (B.

24 20 Show how to make Arby go bankrupt, by giving a list of transactions Arby is willing to make that will guarantee that Arby will lose money (you can assume it will be known whether A occurred and whether B occurred the day after any certificates are bought/sold. Suppose first that P Arby (A B < P Arby (A + P Arby (B. Call a certificate like the one show above, with any event C in place of A, a C-certificate. Measuring money in units of thousands of dollars, Arby is willing to pay P Arby (A + P Arby (B to buy an A-certificate and a B-certificate, and is willing to sell an (A B- certificate for P Arby (A B. In those transactions, Arby loses P Arby (A + P Arby (B P Arby (A B and will not recoup any of that loss because if A or B occurs, Arby will have to pay out an amount equal to the amount Arby receives (since it s impossible for both A and B to occur. Now suppose instead that P Arby (A B > P Arby (A + P Arby (B. Measuring money in units of thousands of dollars, Arby is willing to sell an A-certificate for P Arby (A, sell a B-certificate for P Arby (B, and buy a (A B-certificate for P Arby (A B. In so doing, Arby loses P Arby (A B (P Arby (A+P Arby (B, and Arby won t recoup any of this loss, similarly to the above. (In fact, in this case, even if A and B are not disjoint, Arby will not recoup any of the loss, and will lose more money if both A and B occur. By buying/selling a sufficiently large number of certificates from/to Arby as described above, you can guarantee that you ll get all of Arby s money; this is called an arbitrage opportunity. This problem illustrates the fact that the axioms of probability are not arbitrary, but rather are essential for coherent thought (at least the first axiom, and the second with finite unions rather than countably infinite unions. Arbitrary axioms allow arbitrage attacks; principled properties and perspectives on probability potentially prevent perdition. Inclusion-exclusion 47. A fair die is rolled n times. What is the probability that at least 1 of the 6 values never appears? Let A j be the event that the value j never appears. Then ( n 6 k P (A 1 A 2 A k = 6 for 1 k 5, since there is a 6 k chance that any particular roll is not in {1, 2,..., k}. 6 By inclusion-exclusion and symmetry ( ( n ( ( n ( ( n ( ( n ( n P (A 1 A 6 = ( n ( n ( n ( n ( n = Note that this reduces to 1 for n {1, 2,..., 5}, as it must since there is no way to obtain all 6 possible values in fewer than 6 tosses.