Let X and Y be independent and uniformly distributed random
variables on [0, 1]. Find the...
Let X and Y be independent and uniformly distributed random
variables on [0, 1]. Find the cumulative distribution and
probability density function of Z = X + Y.
Let X and Y be uniformly distributed independent random
variables on [0, 1].
a) Compute the expected value E(XY ).
b) What is the probability density function fZ(z) of Z = XY
?
Hint: First compute the cumulative distribution function FZ(z) =
P(Z ≤ z) using a double integral, and then differentiate in z.
c) Use your answer to b) to compute E(Z). Compare it with your
answer to a).
Let X and Y be independent continuous random variables, with
each one uniformly distributed in the interval from 0 to1. Compute
the probability of the following event.
XY<=1/7
Let Y and Z be independent continuous random variables, both
uniformly distributed between 0 and 1.
1. Find the CDF of |Y − Z|.
2. Find the PDF of |Y − Z|.
Let X, Y and Z be independent random variables, each uniformly
distributed on the interval (0,1).
(a) Find the cumulative distribution function of X/Y.
(b) Find the cumulative distribution function of XY.
(c) Find the mean and variance of XY/Z.
1) Let U1, U2, ... be independent random variables, each
uniformly distributed over the interval (0, 1]. These random
variables represent successive bigs on an asset that you are trying
to sell, and that you must sell by time = t, when the asset becomes
worthless. As a strategy, you adopt a secret number \Theta and you
will accept the first offer that's greater than \Theta . The offers
arrive according to a Poisson process with rate \lambda = 1....
Let X and Y be independent discrete random variables with the
following PDFs:
x
0
1
2
f(x)=P[X=x]
0.5
0.3
0.2
y
0
1
2
g(y)= P[Y=y]
0.65
0.25
0.1
(a) Show work to find the PDF h(w) = P[W=w] = (f*g)(w) (the
convolution) of W = X + Y
(b) Show work to find E[X], E[Y] and E[W] (note that E[W] =
E[X]+E[Y])
1. Let X and Y be independent U[0, 1] random variables, so that
the point (X, Y) is uniformly distributed in the unit square.
Let T = X + Y.
(a) Find P( 2Y < X ).
(b). Find the CDF F(t) of T (for all real numbers t).
HINT: For any number t, F(t) = P ( X <= t) is just the area
of a part of the unit square.
(c). Find the density f(t).
REMARK: For a...
Let X and Y be two independent and identically distributed
random variables with expected value 1 and variance 2.56.
(i) Find a non-trivial upper bound for P(|X + Y − 2| ≥ 1). 5
MARKS
(ii) Now suppose that X and Y are independent and identically
distributed N(1, 2.56) random variables. What is P(|X + Y − 2| ≥ 1)
exactly? Briefly, state your reasoning. 2 MARKS
(iii) Why is the upper bound you obtained in Part (i) so
different...