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)

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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.
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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