2.1 The Polar Method If X and Y are independent standard normal random variables, then the po- lar coordinates, R and are also random variables. From Equation (7), the probability density function for R is f(r) = re-r²; r≥ 0 (8) So observations of R can be generated using the Inverse Cumulative Method. The corresponding observations for are even easier to generate: is uniformly distributed on the interval [0, 27]. Then one can generate a pairs (X,Y) of independent standard normal random variables via: Rcos() Y = Rsin(0) X - (10)
2.1 The Polar Method If X and Y are independent standard normal random variables, then the po- lar coordinates, R and are also random variables. From Equation (7), the probability density function for R is f(r) = re-r²; r≥ 0 (8) So observations of R can be generated using the Inverse Cumulative Method. The corresponding observations for are even easier to generate: is uniformly distributed on the interval [0, 27]. Then one can generate a pairs (X,Y) of independent standard normal random variables via: Rcos() Y = Rsin(0) X - (10)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Fill in the mathematical details of the polar method as described
![### 2.1 The Polar Method
If \( X \) and \( Y \) are independent standard normal random variables, then the polar coordinates, \( R \) and \( \Theta \), are also random variables. From Equation (7), the probability density function for \( R \) is:
\[
f(r) = r e^{-\frac{1}{2} r^2}; \quad r \geq 0
\]
(Equation 8)
So observations of \( R \) can be generated using the Inverse Cumulative Method. The corresponding observations for \( \Theta \) are even easier to generate: \( \Theta \) is uniformly distributed on the interval \([0, 2\pi]\).
Then one can generate pairs \((X, Y)\) of independent standard normal random variables via:
\[
X = R \cos(\Theta)
\]
(Equation 9)
\[
Y = R \sin(\Theta)
\]
(Equation 10)
This mathematical formula and method enable the transformation of polar coordinates into Cartesian coordinates while maintaining the properties of standard normal random variables.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F718b1378-40e4-4c32-83bc-211fc46d7de4%2Fa7e29a2e-45dd-41bf-b792-c1f4050c3fdd%2Fz59ecp4_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### 2.1 The Polar Method
If \( X \) and \( Y \) are independent standard normal random variables, then the polar coordinates, \( R \) and \( \Theta \), are also random variables. From Equation (7), the probability density function for \( R \) is:
\[
f(r) = r e^{-\frac{1}{2} r^2}; \quad r \geq 0
\]
(Equation 8)
So observations of \( R \) can be generated using the Inverse Cumulative Method. The corresponding observations for \( \Theta \) are even easier to generate: \( \Theta \) is uniformly distributed on the interval \([0, 2\pi]\).
Then one can generate pairs \((X, Y)\) of independent standard normal random variables via:
\[
X = R \cos(\Theta)
\]
(Equation 9)
\[
Y = R \sin(\Theta)
\]
(Equation 10)
This mathematical formula and method enable the transformation of polar coordinates into Cartesian coordinates while maintaining the properties of standard normal random variables.
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