Chapter 3: Problem 73
Suppose that we continually roll a die until the sum of all throws exceeds 100 . What is the most likely value of this total when you stop?
Chapter 3: Problem 73
Suppose that we continually roll a die until the sum of all throws exceeds 100 . What is the most likely value of this total when you stop?
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Get started for freeSuppose each new coupon collected is, independent of the past, a type \(i\) coupon with probability \(p_{i} .\) A total of \(n\) coupons is to be collected. Let \(A_{i}\) be the event that there is at least one type \(i\) in this set. For \(i \neq j\), compute \(P\left(A_{i} A_{j}\right)\) by (a) conditioning on \(N_{i}\), the number of type \(i\) coupons in the set of \(n\) coupons; (b) conditioning on \(F_{i}\), the first time a type \(i\) coupon is collected; (c) using the identity \(P\left(A_{i} \cup A_{j}\right)=P\left(A_{i}\right)+P\left(A_{j}\right)-P\left(A_{i} A_{j}\right)\).
A deck of \(n\) cards, numbered 1 through \(n\), is randomly shuffled so that all \(n !\) possible permutations are equally likely. The cards are then turned over one at a time until card number 1 appears. These upturned cards constitute the first cycle. We now determine (by looking at the upturned cards) the lowest numbered card that has not yet appeared, and we continue to turn the cards face up until that card appears. This new set of cards represents the second cycle. We again determine the lowest numbered of the remaining cards and turn the cards until it appears, and so on until all cards have been turned over. Let \(m_{n}\) denote the mean number of cycles. (a) Derive a recursive formula for \(m_{n}\) in terms of \(m_{k}, k=1, \ldots, n-1\). (b) Starting with \(m_{0}=0\), use the recursion to find \(m_{1}, m_{2}, m_{3}\), and \(m_{4}\). (c) Conjecture a general formula for \(m_{n}\). (d) Prove your formula by induction on \(n\). That is, show it is valid for \(n=1\), then assume it is true for any of the values \(1, \ldots, n-1\) and show that this implies it is true for \(n\). (e) Let \(X_{i}\) equal 1 if one of the cycles ends with card \(i\), and let it equal 0 otherwise, \(i=1, \ldots, n\). Express the number of cycles in terms of these \(X_{i}\). (f) Use the representation in part (e) to determine \(m_{n}\). (g) Are the random variables \(X_{1}, \ldots, X_{n}\) independent? Explain. (h) Find the variance of the number of cycles.
An individual whose level of exposure to a certain pathogen is \(x\) will contract the disease caused by this pathogen with probability \(P(x) .\) If the exposure level of a randomly chosen member of the population has probability density function \(f\), determine the conditional probability density of the exposure level of that member given that he or she (a) has the disease. (b) does not have the disease. (c) Show that when \(P(x)\) increases in \(x\), then the ratio of the density of part (a) to that of part (b) also increases in \(x\).
A coin that comes up heads with probability \(p\) is continually flipped until the pattern \(\mathrm{T}, \mathrm{T}, \mathrm{H}\) appears. (That is, you stop flipping when the most recent flip lands heads, and the two immediately preceding it lands tails.) Let \(X\) denote the number of flips made, and find \(E[X]\).
Independent trials, each resulting in success with probability \(p\), are performed. (a) Find the expected number of trials needed for there to have been both at least \(n\) successes or at least \(m\) failures. Hint: Is it useful to know the result of the first \(n+m\) trials? (b) Find the expected number of trials needed for there to have been either at least \(n\) successes or at least \(m\) failures. Hint: Make use of the result from part (a).
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