7.1 required non-book problem: Suppose RV Y is continuous with invertible CDF Fy. Then 1. U...
5. (20 pts) Function of RV Let Ry X-Exponential(1),i.e.,the CDF is Fx (x) = (1 - )u(x). IEX = 9(x) = -2x + 1, find the CDF Fy (y) and the PDF fy(y).
1. Let U be a random variable that is uniformly distributed on the interval (0,1) (a) Show that V 1 - U is also a uniformly distributed random variable on the interval (0,1) (b) Show that X-In(U) is an exponential random variable and find its associated parameter (c) Let W be another random variable that is uformly distributed on (0,1). Assume that U and W are independent. Show that a probability density function of Y-U+W is y, if y E...
Let U U (0,1) and let Y=1-U. Derive an expression for the odf Fy() of Y in terms of the odf of U and hence show that Y U (0,1).
Let X1 d = R(0,1) and X2 d= Bernoulli(1/3) be two independent random variables, define Y := X1 + X2 and U := X1X2. (a) Find the state space of Y and derive the cdf FY and pdf fY of Y . (You may wish to use {X2 = i}, i = 0,1, as a partition and apply the total probability formula.) (b) Compute the mean and variance of Y in two different ways, one is through the pdf of...
STAT 115 Let X be a continuous random variable having the CDF Fx(x) = 1 - e^ (-e^x) (1) Find the Probability Density Function (PDF) of Y=e^X. (2) Let B have a uniform distribution over (0,1). Find a function G(b) and G(B) has the same distribution as X.
Suppose that you need to generate a random variable Y with a density function f (y) corresponding to a beta distribution with range [0,1], and with a non-integer shape parameter for the beta distribution. For this case there is no closed-form cdf or inverse cdf. Suppose your choices for generating Y are either: a) an acceptance-rejection strategy with a constant majorizing function g(u) = V over [0, 1], i.e., generate u1 and u2 IID from a U[0,1] generator and accept...
Problem # 8. a) Let X be a continuous random variable with known CDF FX(x). LetY = g(X) where g(·) is the so-called signum function, which extracts the sign of its argument. In other words, g(X) = { -1 x<0, 0 x=0, 1 x>0 } Express the PDF fY (y) in terms of the known CDF FX(x). b) Let X be a random variable with PDF: fX(x) = { x/2 0 <= x < 2, 0 otherwise} Let Y be...
[25 points] Problem 4 - CDF Inversion Sampling ers coming from the U(0, 1) distribution into In notebook 12, we looked at one method many pieces of statistical software use to turn pseudorandom those with a normal distribution. In this problem we examine another such method. a) Simulating an Exponential i) The exponential distribution has pdf f(x) = le-ix for x > 0. Use the following markdown cell to compute by hand the cdf of the exponential. ii) The cdf...
I can do the first part of the question 1a, could someone show me step by step how to do do 1b? ) Y.Ya..., Y, form a random sample from a probability distribution with cumu- lative distribution function Fy (u) and probability density function fr(u). Let Write the cumulative distribution function for Ya) in terms of Fy(y) and hence show that the probability density function for Yy is fy(1)(y) = n(1-Fr (v))"-ify(y). [8 marks] (b) An engineering system consists of...
5. Suppose X has the Rayleigh density otherwise 0, a. Find the probability density function for Y-X using Theorem 8.1.1. b. Use the result in part (a) to find E() and V(). c. Write an expression to calculate E(Y) from the Rayleigh density using LOTUS. Would this be easier or harder to use than the above approach? of variables in one dimension). Let X be s Y(X), where g is differentiable and strictly incr 1 len the PDF of Y...