Probability and Random Processes Midterm 2: EECS 126, Exams of Probability and Statistics

The questions and instructions for the midterm exam of the probability and random processes course (eecs 126) held on november 12, 2002. The exam covers topics such as independent random variables, generating random variables, continuous and discrete random variables, and minimum mean square error estimation.

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EECS 126: Probability and Random Processes
Midterm 2
Tuesday, November 12, 2002 3:40-5 pm
Answer all questions on the attached blank sheets. Please explain your reasoning carefully.
Answers without reasoning will get no marks. The total is 100 points. Q. 2 (d) is a bonus
part worth 15 points.
[20] 1. Let Xand Ybe independent RV’s, both exponentially distributed with mean 1.
Let Z= max(X, Y ).
[10] a) Compute the pdf of Xconditional on the event that Zz.
[10] b) Xand Yare independent, but conditional on Z=z, are they still independent?
Explain. (Hint: no calculations needed.)
[35] 2.
[10] a) Suppose you have access to a RV XN(µ1, σ2
1). Explain how you can use Xto
generate a RV Ywhich is N(µ2, σ2
2).
[10] b) Suppose you have access to a RV Xuniform distributed in [0,1]. Explain how you
can use Xto generate a continuous RV Ywith a given pdf fYwhich is nonzero everywhere.
[15] c) Suppose you have access to a RV Xuniform distributed in [0,1]. Explain how you
can use Xto generate a discrete RV Ywith a given pmf pY(·). You can assume that the
range of values Ycan take on is finite. (Hint: try the simple case when Yis Bernouilli
(i.e.Ytakes on only two possible values) and then attack the general case.)
[15] d) (Bonus) Suppose you have access to two indpendent RVs Xand Yboth uniform
distributed in [0,1]. Explain how you can use them to generate two continuous RV’s Uand
Vwith a given joint density fU,V which is nonzero everywhere. (Hint: you may want to
consider a sequential procedure where you first generate Uand then generate V.)
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EECS 126: Probability and Random Processes

Midterm 2

Tuesday, November 12, 2002 3:40-5 pm

Answer all questions on the attached blank sheets. Please explain your reasoning carefully. Answers without reasoning will get no marks. The total is 100 points. Q. 2 (d) is a bonus part worth 15 points.

[20] 1. Let X and Y be independent RV’s, both exponentially distributed with mean 1. Let Z = max(X, Y ).

[10] a) Compute the pdf of X conditional on the event that Z ≤ z.

[10] b) X and Y are independent, but conditional on Z = z, are they still independent? Explain. (Hint: no calculations needed.)

[35] 2.

[10] a) Suppose you have access to a RV X ∼ N (μ 1 , σ 12 ). Explain how you can use X to generate a RV Y which is N (μ 2 , σ^22 ).

[10] b) Suppose you have access to a RV X uniform distributed in [0, 1]. Explain how you can use X to generate a continuous RV Y with a given pdf fY which is nonzero everywhere.

[15] c) Suppose you have access to a RV X uniform distributed in [0, 1]. Explain how you can use X to generate a discrete RV Y with a given pmf pY (·). You can assume that the range of values Y can take on is finite. (Hint: try the simple case when Y is Bernouilli (i.e.Y takes on only two possible values) and then attack the general case.)

[15] d) (Bonus) Suppose you have access to two indpendent RVs X and Y both uniform distributed in [0, 1]. Explain how you can use them to generate two continuous RV’s U and V with a given joint density fU,V which is nonzero everywhere. (Hint: you may want to consider a sequential procedure where you first generate U and then generate V .)

[45] 3.

a) A continuous random variable U has a pdf of the form:

fU (u) = a exp(bu^2 + cu), −∞ < u < ∞.

where a, b, c are constants. a and b are non-zero.

[10] i) Can you say anything definitive about the signs of a, b and c?

[10] ii) Does U have to be Gaussian? If so, express its mean and variance in terms of a, b and c in the simplest way. If not, give an example of U with pdf of the above form but is not Gaussian.

[5] b) Let X ∼ N (0, v^2 ), Z ∼ N (0, σ^2 ) and we have a noisy observation:

Y = X + Z

The RVs X and Z are independent. Find from first principles the MMSE estimate of X given Y and the resulting minimum mean square error. (Hint: you may find your answer to part (a)(ii) useful in simplifying the calculations.)

c) Suppose we now have n noisy observations:

Yi = X + Zi, i = 1, 2... n, where the Zi’s are i.i.d. N (0, σ^2 ) noise and independent of X.

[14] i) Find from first principles the MMSE estimate of X given Y 1 ,... , Yn and the resulting minimum mean square error. (Hint: you may find your answer to part (a)(ii) VERY useful in simplifying the calculations.)

[3] ii) Explain intuitively the effect of the parameter v^2 on the MMSE estimator. How does the relative role of v^2 change when n becomes large?

[3] iii) What happens to the minimum mean-square error when n is large? Give an intuitive explanation.