Homework One for Computer Systems Analysis | CS 541, Assignments of Computer Science

Material Type: Assignment; Professor: Nicol; Class: Computer Systems Analysis; Subject: Computer Science; University: University of Illinois - Urbana-Champaign; Term: Fall 2008;

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ECE/CS 541: COMPUTER SYSTEM ANALYSIS
Homework #1 – Due Tuesday, September 1, Beginning of Class
August 25, 2008
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.
1. Consider the following program segment:
if B then
repeat S1 until B1
else
repeat S2 until B2
Assume that P(B = true) = p, P(B1 = true) = 5/8, and P(B2 = true) = 3/8. Exactly one statement is
common to statement groups S1 and S2: 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.
2. In manufacturing a certain component, two types of defects are likely to occur with respective prob-
abilities 0.05 and 0.1. Assuming that the defects are independent of one another, what is the probabil-
ity that a randomly chosen component:
(a) Does not have both defects?
(b) Is defective?
(c) Has only one kind of defect, given that it is found to be defective?
3. A mischievous student wants to break into a computer file, which is password-protected. Assume
that there are n equally likely passwords, and that the student chooses passwords independently and at
random and tries them. Let Nn be the number of trials required to break into the file. Determine the
probability density function of Nn (a) if unsuccessful passwords are not eliminated from further selec-
tions, and (b) if they are.
4. Assume that the number of messages input to a communication channel in an interval of duration t
seconds is Poisson distributed with parameter 0.3t. Compute the probabilities of the following
events:
(a) Exactly two messages will arrive during a five-second interval,
(b) At most eight messages arrive in a period of ten seconds,
(c) The number of message arrivals in an interval of duration twenty seconds is between eight and
twelve.
5. Consider a communication channel, where the probability of error-free transmission of a packet has
some fixed value p. Suppose further that if a packet is erroneous when received, a retransmission is
initiated. This is repeated until an error-free transmission occurs. Moreover, the outcome of each
transmission is independent of previous transmissions. To put this procedure in a more formal set-
pf2

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ECE/CS 541: COMPUTER SYSTEM ANALYSIS

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.

  1. Consider the following program segment:

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.

  1. In manufacturing a certain component, two types of defects are likely to occur with respective prob- abilities 0.05 and 0.1. Assuming that the defects are independent of one another, what is the probabil- ity that a randomly chosen component:

(a) Does not have both defects? (b) Is defective? (c) Has only one kind of defect, given that it is found to be defective?

  1. A mischievous student wants to break into a computer file, which is password-protected. Assume that there are n equally likely passwords, and that the student chooses passwords independently and at random and tries them. Let Nn be the number of trials required to break into the file. Determine the probability density function of Nn (a) if unsuccessful passwords are not eliminated from further selec- tions, and (b) if they are.
  2. Assume that the number of messages input to a communication channel in an interval of duration t seconds is Poisson distributed with parameter 0.3 t. Compute the probabilities of the following events: (a) Exactly two messages will arrive during a five-second interval, (b) At most eight messages arrive in a period of ten seconds, (c) The number of message arrivals in an interval of duration twenty seconds is between eight and twelve.
  3. Consider a communication channel, where the probability of error-free transmission of a packet has some fixed value p. Suppose further that if a packet is erroneous when received, a retransmission is initiated. This is repeated until an error-free transmission occurs. Moreover, the outcome of each transmission is independent of previous transmissions. To put this procedure in a more formal set-

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.

  1. Let X = { X (^) t | t % T } be an independent stochastic process, let n be a positive integer, let ( t (^) 1 , t 2 , ... t (^) n ) be a sequence of distinct times ( t (^) i % T ) and consider the random variable Y :! & ' (! is the sample space for variables X (^) t ) where

Y ( # ) ,minimum* X t 1 (# ) , Xt 2 (# ) ,..., Xtn (# )+

Express the cumulative distribution function (cdf) of Y in terms of Xt 1 , Xt 2 , Xt 3 ,... Xtn.

  1. Suppose the variables Xt of the process in the previous problem are identically distributed with an exponential distribution having parameter - (- > 0), i.e., for all t $ in T ,

Fx t ( ) x , 1. e.^ - x

Determine the cumulative distribution function (cdf) and probability density funtion (pdf) of the random variable Y defined in the previous problem.

  1. Let X be the stochastic process considered in Problem 7 and suppose further X is discrete-time with T = {0,1,2,...}. Consider now the “random sum”

.

,

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.