The “random” parts of the algorithm in Self-Test Problem 6.9 &1 can be written in terms of the generated values of a sequence of independent uniform (0, 1) random variables, known as random numbers. With [x] defined as the largest integer less than or equal to x, the first step can be written as follows: Step 1. Generate a uniform (0, 1) random variable U. Let X = [ m U ] + 1 and determine the value of n ( X ) . a. Explain why the above is equivalent to step I of Problem 6.8. Hint: What is the probability mass function of X? b. Write the remaining steps of the algorithm in a similar style.
The “random” parts of the algorithm in Self-Test Problem 6.9 &1 can be written in terms of the generated values of a sequence of independent uniform (0, 1) random variables, known as random numbers. With [x] defined as the largest integer less than or equal to x, the first step can be written as follows: Step 1. Generate a uniform (0, 1) random variable U. Let X = [ m U ] + 1 and determine the value of n ( X ) . a. Explain why the above is equivalent to step I of Problem 6.8. Hint: What is the probability mass function of X? b. Write the remaining steps of the algorithm in a similar style.
Solution Summary: The author explains the relation between probability mass function and probability of selecting page out of m pages.
The “random” parts of the algorithm in Self-Test Problem 6.9 &1 can be written in terms of the generated values of a sequence of independent uniform (0, 1) random variables, known as random numbers. With [x] defined as the largest integer less than or equal to x, the first step can be written as follows:
Step 1. Generate a uniform (0, 1) random variable U. Let
X
=
[
m
U
]
+
1
and determine the value of
n
(
X
)
.
a. Explain why the above is equivalent to step I of Problem 6.8.
Hint: What is the probability mass function of X?
b. Write the remaining steps of the algorithm in a similar style.
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1.1 ACSF L5 SC Geometry and Measure: Vectors
Vectors
State the vector quantities shown on the image below.
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2. Claim events on a portfolio of insurance policies follow a Poisson process with parameter
A. Individual claim amounts follow a distribution X with density:
f(x)=0.0122re001, g>0.
The insurance company calculates premiums using a premium loading of 45%.
(a) Derive the moment generating function Mx(t).
2. Claim events on a portfolio of insurance policies follow a Poisson process with parameter
A. Individual claim amounts follow a distribution X with density:
f(x)=0.0122re001, g>0.
The insurance company calculates premiums using a premium loading of 45%.
(a) Derive the moment generating function Mx(t).
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