Chapter 10: Q. 10.8 (page 431)
Suppose it is relatively easy to simulate from Fi for each i = 1, ... , n. How can we simulate from
(a)
(b)
Short Answer
(a) The CDF is the CDF of maximum.
(b) The CDF is the CDF of minimum.
Chapter 10: Q. 10.8 (page 431)
Suppose it is relatively easy to simulate from Fi for each i = 1, ... , n. How can we simulate from
(a)
(b)
(a) The CDF is the CDF of maximum.
(b) The CDF is the CDF of minimum.
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Get started for freeIn Example 2c we simulated the absolute value of a unit normal by using the rejection procedure on exponential random variables with rate 1. This raises the question of whether we could obtain a more efficient algorithm by using a different exponential density—that is, we could use the density g(x) = λe−λx. Show that the mean number of iterations needed in the rejection scheme is minimized when λ = 1.
Use the inverse transformation method to present an approach for generating a random variable from the Weibull distribution
Give an efficient algorithm to simulate the value of a random variable with probability mass function
p1 = .15 p2 = .2 p3 = .35 p4 = .30
Let F be the distribution function
F(x) = xn 0 < x < 1
(a) Give a method for simulating a random variable having distribution F that uses only a single random number.
(b) Let U1, ... , Un be independent random numbers. Show that
P{max(U1, ... , Un) … x} = xn
(c) Use part (b) to give a second method of simulating a random variable having distribution F.
Use the rejection method with g(x) = 1, 0 < x < 1, to determine an algorithm for simulating a random variable having density function
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