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- Functions of random variables
- Problems on functions of random
Suppose
X is a nonnegative, absolutely continuous random variable. Let
, where
. Then
. Use properties
of the exponential and natural log function to show that
iff
iff
iff
, so that
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Present value of future costs. Suppose money may be invested at an
annual rate
a , compounded continually. Then one dollar in hand now, has a value
at the end of
x years. Hence, one dollar spent
x years in the future has a
present value
. Suppose a device put into operation has time to
failure (in years)
exponential
. If the cost of replacement
at failure is
C dollars, then the present value of the replacement is
.
Suppose
,
, and
.
- Use the result of
[link] to determine the probability
.
- Use a discrete approximation for the exponential density to approximate the
probabilities in part (a). Truncate
X at 1000 and use 10,000 approximation points.
v = [700 500 200];P = (v/1000).^(10/7)
P = 0.6008 0.3715 0.1003tappr
Enter matrix [a b]of x-range endpoints [0 1000]
Enter number of x approximation points 10000Enter density as a function of t 0.1*exp(-t/10)
Use row matrices X and PX as in the simple caseG = 1000*exp(-0.07*t);
PM1 = (G<=700)*PX'
PM1 = 0.6005PM2 = (G<=500)*PX'
PM2 = 0.3716PM3 = (G<=200)*PX'
PM3 = 0.1003
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Optimal stocking of merchandise. A merchant is planning for the Christmas season. He intends to stock
m units of a
certain item at a cost of
c per unit. Experience indicates demand can be represented
by a random variable
Poisson
. If units remain in stock at the
end of the season, they may be returned with recovery of
r per unit. If demand
exceeds the number originally ordered, extra units may be ordered at a cost of
s each. Units are sold at a price
p per unit.
If
is the gain from the sales, then
- For
- For
Let
. Then
Suppose
.
Approximate
the Poisson random variable
D by truncating at 100. Determine
.
mu = 50;
D = 0:100;c = 30;
p = 50;r = 20;
s = 40;m = 50;
PD = ipoisson(mu,D);G = (p - s)*D + (s - c)*m +(s - r)*(D - m).*(D<= m);
M = (500<=G)&(G<=1100);
PM = M*PD'PM = 0.9209[Z,PZ] = csort(G,PD); % Alternate: use dbn for Zm = (500<=Z)&(Z<=1100);
pm = m*PZ'pm = 0.9209
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(See
Example 2 from "Functions of a Random Variable") The cultural committee of a student organization has arranged a
special deal for tickets to a concert. The agreement is that the organization will purchase tentickets at $20 each (regardless of the number of individual buyers). Additional tickets are
available according to the following schedule:
- 11-20, $18 each
- 21-30, $16 each
- 31-50, $15 each
- 51-100, $13 each
If the number of purchasers is a random variable
X , the total cost (in dollars) is
a random quantity
described by
Suppose
Poisson (75). Approximate the Poisson distribution by truncating at 150.
Determine
, and
.
X = 0:150;
PX = ipoisson(75,X);G = 200 + 18*(X - 10).*(X>=10) + (16 - 18)*(X - 20).*(X>=20) + ...
(15 - 16)*(X- 30).*(X>=30) + (13 - 15)*(X - 50).*(X>=50);
P1 = (G>=1000)*PX'
P1 = 0.9288P2 = (G>=1300)*PX'
P2 = 0.1142P3 = ((900<=G)&(G<=1400))*PX'
P3 = 0.9742[Z,PZ] = csort(G,PX); % Alternate: use dbn for Zp1 = (Z>=1000)*PZ'
p1 = 0.9288
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Source:
OpenStax, Applied probability. OpenStax CNX. Aug 31, 2009 Download for free at http://cnx.org/content/col10708/1.6
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