From the definition of expectation, we know that
Hence, using the definition of modulus function we have
Differentiate with respect to we get
Let be the value which
minimizes
Then
Now, we know that
Hence, by (A) the required equation is
2. Let X be a continuous r.v. with pdf fx() for which EX exists. We want to find the real number ...
2. Let X be a continuous r.v. with pdf f () and cdf F(x). Let U F (X). Show that, as long as F(x) is strictly monotonic increasing, U is uniformly distributed on (0,1). Discuss why this result is important, given that it is known how to simulate Uniformly distributed random variables easily.
Let X be a continuous random variable with PDF fx(x)- 0 otherwise We know that given Xx, the random variable Y is uniformly distributed on [-x,x. 1. Find the joint PDF fx(x, y) 2. Find fyo). 3. Find P(IYI <x3)
Let X be a continuous random variable with PDF fx(x)- 0 otherwise We know that given Xx, the random variable Y is uniformly distributed on [-x,x. 1. Find the joint PDF fx(x, y) 2. Find fyo). 3. Find P(IYI
4. (20%) Let X be a continuous random variable with the following PDF Sce-4x 0<x fx(x) = to else where c is a positive constant. (a) (5%) Find c. (b) (5%) Find the CDF of X, Fx(x). (c) (5%) Find Prob{2<x<5} (d)(5%) Find E[X], and Var(X).
For a continuous variable X with the following PDF: 0sxs2 fx (x) = {2' 0, otherwise (a) Determine the conditional PDF of X given that X>1. (b) Find the conditional CDF of X given that X > 1, and plot the corresponding figure with proper labels. [Note: Both the expression and the plot are required.]
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.
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...
Let $(x) = 2x2 and let Y = $(X). assume that Y ~ U(0,1/2) and that X is a continuous random variable. fx(x) = 0 whenever |2| > 1. Obtain an expression linking fx(x) to fx(-x) for xe (-1,1). Show that E[X] = -2/3 + 28. xfx(x) dx. Using your expression linking fx(x) and fx(-x), obtain an upper bound for E[X] and a pdf fx for which this bound is attained. [10]
PROBLEM 4 Let X be a continuous random variable with the following PDF 6x(1 - 1) if 0 <r<1 fx(x) = o.w. Suppose that we know Y X = ~ Geometric(2). Find the posterior density of X given Y = 2, i.e., fxy (2/2).
P7
continuous random variable X has the probability density function fx(x) = 2/9 if P.5 The absolutely continuous random 0<r<3 and 0 elsewhere). Let (1 - if 0<x< 1, g(x) = (- 1)3 if 1<x<3, elsewhere. Calculate the pdf of Y = 9(X). P. 6 The absolutely continuous random variables X and Y have the joint probability density function fx.ya, y) = 1/(x?y?) if x > 1,y > 1 (and 0 elsewhere). Calculate the joint pdf of U = XY...
5. Lec 17 function of pairs of R.V., 8 pts) Let X be the lifetime of a critical and expensive component in a system, which is exponentially distributed with mean 2 years. The system also has a cheaper backup component that can take over when the expensive component fails so that the system can provide continuous service while the more expensive system is being repaired. Let Y be the lifetime of the backup system, which is also exponentially distributed but...