Chapter 3: Problem 69
In the match problem, say that \((i, j), i
Chapter 3: Problem 69
In the match problem, say that \((i, j), i
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Get started for freeIndependent trials, resulting in one of the outcomes \(1,2,3\) with respective probabilities \(p_{1}, p_{2}, p_{3}, \sum_{i=1}^{3} p_{i}=1\), are performed. (a) Let \(N\) denote the number of trials needed until the initial outcome has occurred exactly 3 times. For instance, if the trial results are \(3,2,1,2,3,2,3\) then \(N=7\) Find \(E[N]\). (b) Find the expected number of trials needed until both outcome 1 and outcome 2 have occurred.
\(A, B\), and \(C\) are evenly matched tennis players. Initially \(A\) and \(B\) play a set, and the winner then plays \(C\). This continues, with the winner always playing the waiting player, until one of the players has won two sets in a row. That player is then declared the overall winner. Find the probability that \(A\) is the overall winner.
Let \(X\) be exponential with mean \(1 / \lambda ;\) that is,
$$
f_{X}(x)=\lambda e^{-\lambda x}, \quad 0
In the list problem, when the \(P_{i}\) are known, show that the best ordering (best in the sense of minimizing the expected position of the element requested) is to place the elements in decreasing order of their probabilities. That is, if \(P_{1}>P_{2}>\cdots>P_{n}\) show that \(1,2, \ldots, n\) is the best ordering.
Let \(X_{1}, \ldots, X_{n}\) be independent random variables having a common distribution function that is specified up to an unknown parameter \(\theta\). Let \(T=T(\mathrm{X})\) be a function of the data \(\mathrm{X}=\left(X_{1}, \ldots, X_{n}\right) .\) If the conditional distribution of \(X_{1}, \ldots, X_{n}\) given \(T(\mathrm{X})\) does not depend on \(\theta\) then \(T(\mathrm{X})\) is said to be a sufficient statistic for \(\theta .\) In the following cases, show that \(T(\mathbf{X})=\sum_{i=1}^{n} X_{i}\) is a sufficient statistic for \(\theta\). (a) The \(X_{i}\) are normal with mean \(\theta\) and variance \(1 .\) (b) The density of \(X_{i}\) is \(f(x)=\theta e^{-\theta x}, x>0\). (c) The mass function of \(X_{i}\) is \(p(x)=\theta^{x}(1-\theta)^{1-x}, x=0,1,0<\theta<1\). (d) The \(X_{i}\) are Poisson random variables with mean \(\theta\).
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