Description
Probabilistic Methods in Electrical and Computer Engineering
Exercise 1.
Two dice are tossed. Let X be the absolute difference in the number of dots facing up. Let
g(X) = (
X2
, if X > 2
0, otherwise.
and h(X) = −|X − 2|.
(a) Find E[g(X)].
(b) Find E[h(X)].
Exercise 2.
A modem transmits a +2 voltage signal into a channel. The channel adds to this signal a noise term that is
drawn from the set {0, −1, −2, −3} with respective probabilities {1/10, 2/10, 3/10, 4/10}.
(a) Find and sketch the PMF and the CDF of the output Y of the channel.
(b) What is the probability that the output of the channel is equal to the input of the channel?
(c) What is the probability that the output of the channel is positive?
(d) Find the expected value and variance of Y .
Exercise 3.
A voltage X is uniformly distributed in the set {0, 1, 2, 3}.
(a) Find the mean and variance of X.
(b) Find the mean and variance of Y = X2 − 2.
(c) Find the mean of W = sin(πX/4).
(d) Find the mean of Z = sin2
(πX/4).
Exercise 4.
(a) If X is Poisson(λ), compute E[3/(X + 2)].
(b) If X is Bernoulli(p) and Y is Bernoulli(q), compute E[(2X + Y )
2
] if X and Y are independent.
Exercise 5.
Let X be the number of photons counted by a receiver in an optical communication system. It is known
that X is a Poisson random variable with rate λ1 when a signal is present and a Poisson random variable
with rate λ0 < λ1 when a signal is absent. The probability that the signal is present is p. Suppose that we
observe X = k photons. We want to determine a threshold T such that if k ≥ T then we claim that the
signal is present; and if k < T then we claim that the signal is absent. What is this T?
Exercise 6.
A random variable X has PDF:
fX(x) = (
cx(4 − x
2
), 0 ≤ x ≤ 2
0, otherwise.
(a) Find c
(b) Find FX(x)
(c) Find E[X]
Exercise 7.
Consider a CDF
FX(x) =
0, if x < −2
0.25, if − 2 ≤ x < 0
(x + 1)/2, if 0 ≤ x < 1
1, otherwise.
(a) Find P[X < −2], P[X = 0] and P[X > 0.5].
(b) Find fX(x).
Exercise 8.
Consider a discrete PMF pX(k) =
0.3 0.1 0.15 0.25 0.1 0.08 0.02
. Write a MATLAB / Python
function that takes this PMF and generates N = 100, 000 realizations of X. Your function can only use
the uniform random number generator rand in MATLAB (or numpy.random.rand in Python) and no other
random number generators. Submit your code and the empirical histogram of X.
Please attach your code and plot after this page.
