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Material Type: Assignment; Professor: Nicol; Class: Computer Systems Analysis; Subject: Computer Science; University: University of Illinois - Urbana-Champaign; Term: Fall 2008;
Typology: Assignments
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Homework #1 – Due Tuesday, September 1 , Beginning of Class
August 25 , 200 8
The intent of this homework is to review material you should have learned in your probability and statis- tics course (e.g., ECE 413) and explore new material regarding probability theory and stochastic proc- esses we have discussed in class. They vary in difficulty from those you should be able to solve in a cou- ple of minutes, to those that require more thought. If you have trouble with these problems, you should review material from your probability course, or come and talk to me during office hours.
if B then repeat S (^) 1 until B (^) 1 else repeat S (^) 2 until B (^) 2
Assume that P ( B = true ) = p , P ( B (^) 1 = true ) = 5 / 8 , and P(B (^) 2 = true ) = 3 / 8. Exactly one statement is common to statement groups S (^) 1 and S 2 : write (‘good day’). After many repeated executions of the program segment, it has been estimated that the probability of printing exactly three ‘good day’ mes- sages is 15 / 128. Derive the value of p.
(a) Does not have both defects? (b) Is defective? (c) Has only one kind of defect, given that it is found to be defective?
ting, let X be a random variable, where X = the number of retransmissions required for error-free re- ception. (If the initial transmission is error-free, X = 0.)
(a) Construct a probability space (!, ", P ) which suffices to serve as the underlying space for X. (Regarding !, you can get by with a sample space that’s countably infinite.) With regard to the measure P , for any singleton event {#} (#$% !), you should be able to give a formula that ex- presses the probability P ({#}) in terms of #. (You need not formulate P for larger events; their values follow from axioms and theorems for a probability space.)
(b) Since X is a discrete-valued random variable, its probabilistic nature can be captured by its prob- ability distribution (PD), i.e., the probabilities { P [ X = 0], P [ X = 1], ...}. Give a general formula which, for k % {0, 1, ...}, expresses the probability P [ X = k ]. (You may already have computed this as part of your answer to part (a).)
(c) Formulate the expected value E [ X ] and the variance Var[ X ] of this random variable.
Express the cumulative distribution function (cdf) of Y in terms of Xt 1 , Xt 2 , Xt 3 ,... Xtn.
Determine the cumulative distribution function (cdf) and probability density funtion (pdf) of the random variable Y defined in the previous problem.
.
,
1
0
N
t
SN Xt
where N is a geometrically distributed random variable, i.e., for k % {1, 2, ...}
P [ N = k ] = r (1 - r ) k^ - 1
with r some real number, 0 < r < 1. Prove that S (^) N is exponentially distributed.