Conditioning
In the discrete case, the conditional PMF of \(X\) given \(Y=y\) is defined by
\[
p_{X \mid Y}(x \mid y)=\frac{p_{X, Y}(x, y)}{p_Y(y)}, \;\; \text{if } p_Y(y)>0.
\]
In the continuous case, the conditional PDF of \(X\) given \(Y=y\) is defined by
\[
f_{X \mid Y}(x \mid y)=\frac{f_{X, Y}(x, y)}{f_Y(y)}, \;\; \text{if } f_Y(y)>0.
\]
\[
p_{X \mid Y}(x \mid y)=\frac{p_{X, Y}(x, y)}{p_Y(y)}
\]
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\(p_{X \mid Y}(x \mid y)\) has the same shape as \(p_{X, Y}(x, y)\) at \(Y=y\) except that it is divided by \(p_Y(y)\), which ensure the normalization property.
\[
f_{X \mid Y}(x \mid y)=\frac{f_{X, Y}(x, y)}{f_Y(y)}
\]
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\(f_{X \mid Y}(x \mid y)\) has the same shape as \(f_{X, Y}(x, y)\) at \(Y=y\) except that it is divided by \(f_Y(y)\), which enforce the normalization property.
Proof \[
\begin{aligned}
\int_{-\infty}^{+\infty} f_{X \mid Y}(x \mid y)dx&=\int_{-\infty}^{+\infty} \frac{f_{X,Y}(x,y)}{f_Y(y)}dx \\
\\
&=\frac{1}{f_Y(y)} \int_{-\infty}^{+\infty} f_{X,Y}(x,y)dx \\
\\
&=\frac{1}{f_Y(y)} \; f_Y(y) \\
\\
&=1 \\
\end{aligned}
\]
The multiplication rule
In the discrete case,
\[
\begin{aligned}
p_{X, Y}(x, y) &= p_X(x) \cdot p_{Y \mid X}(y \mid x) \\
&= p_Y(y) \cdot p_{X \mid Y}(x \mid y) \\
\end{aligned}
\]
We can use this formula to calculate the joint PMF.
In the continuous case,
\[
\begin{aligned}
f_{X, Y}(x, y)&= f_Y(y) \cdot f_{X \mid Y}(x \mid y) \\
&= f_X(x) \cdot f_{Y \mid X}(y \mid x) \\
\end{aligned}
\]
We can use this formula to calculate the joint PDF.
How are the joint, marginal, and conditional PDFs related to each other?
To calculate the joint PDF,
\[
f_{X, Y}(x, y)= f_Y(y) \cdot f_{X \mid Y}(x \mid y)
\]
To calculate the marginal PDF
\[
\begin{aligned}
f_X(x)&=\int_{-\infty}^{+\infty} f_{X, Y}(x, y)dy \\
&=\int_{-\infty}^{+\infty} f_Y(y) \cdot f_{X \mid Y}(x \mid y)dy \\
\end{aligned}
\]
