91Ó°ÊÓ

The statistical distribution with a probability density function $\left(1\right)$is known as the bivariate normal distribution.

Let $Z_1$and $Z_2$be standard normal random variables that are independent.

$Z_1$and $Z_2$are independent based on the information provided, and their covariance is zero.

The two standard normal random variables' probability density function is.

$$\begin{gathered} f\left(Z_1\right)=\frac{1}{\sqrt{2\mathrm{Ï€}}}{e}^{-t_1^2/2} \\ f\left(Z_2\right)=\frac{1}{\sqrt{2\mathrm{Ï€}}}{e}^{-z_2^2/2} \end{gathered}$$

Since, $Z_1$and $Z_2$are two independent variables so by independence joint distribution of them will be product of their marginal density function is $f(Z_1,Z_2)=\frac{1}{2\mathrm{π}}{e}^{\frac{1}{2}({\mathrm{θ}}^2+t_2^2)}$.

Based on the information provided $X=Z_1$and

role="math" localid="1647250214809" $$\begin{gathered} Y=Z_1+Z_2 \\ ⇒Z_2=Y-X \end{gathered}$$

Using the Jacobian transformation ,

$$\begin{gathered} J=\left|\begin{array}{cc}\frac{∂x}{∂u}& \frac{∂x}{∂v}\\ \frac{∂y}{∂u}& \frac{∂y}{∂v}\end{array}\right| \\ =\left|\begin{array}{cc}1& 0\\ -1& 1\end{array}\right| \\ =1 \end{gathered}$$

So, the joint density of X and Y will be,

$$\begin{gathered} f_{xy}(x,y)=\frac{1}{2\mathrm{Ï€}}{e}^{-\frac{1}{2}(z^2+a_i^2)}\left|J\right| \\ =\frac{1}{2\mathrm{Ï€}}{e}^{\frac{-(x^2+{(y-x)}^2)}{2}}\left|1\right| \\ =\frac{1}{2\mathrm{Ï€}}{e}^{\frac{-(2x^2+2y+y^2)}{2}} \end{gathered}$$

Hence, this is probability density function of a bivariate normal distribution.