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21:17
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A: Density of $X_1 + X_2$ using convolution

Math LoverWe have $~X \sim U(-1, 1)$ and $Y \sim U(0, 5)$. For distribution of $Z = X + Y, ~F_Z(z) = P(Y \lt z - x)$ Now as we must have $0 \lt y \lt z - x \lt 5$, $(i)$ for $- 1 \lt z \lt 1, - 1 \lt x \lt z $ $(ii)$ For $1 \lt z \lt 4, -1 \lt x \lt 1$ $(iii)$ For $4 \lt z \lt 6$, if $~-1 \lt x \lt z-5, 0...

So we have that $$F_Z(z) = P(Y \lt z - x)=\int_{\infty}^{z-x}f_Y(y)\, dy$$ right? How do we use now the intervals as for $z$ ? I got stuck right now.
If $-1 \lt z \lt 1, F_Z(z) = P(Y \lt z - x) = $ $ \displaystyle \int_{-1}^{z} \int_0^{z-x} f(x, y) \, dy \, dx$. Note $f(x, y) = \dfrac{1}{10}$
Why do we have a double integral if we have just the random variable $Y$ at the left side ? That part is not clear for me. Could you explain that further to me ?
because $ \displaystyle F_Z(z) = \int_{-\infty}^{\infty} F_Y(z-x) f_X(x) ~ dx$, $f_X(x) = \frac 12$ and $ \displaystyle F_Y(z-x) = \int_0^{z-x} f_Y(y) ~ dy$
You don't have to write double integral. Knowing $F_Y(z-x) = \frac{z-x}{5}$, you could write as $ \displaystyle \int_{-1}^z \frac{z-x}{5} \cdot \frac 12 ~ dx$
Why does it hold that $\displaystyle F_Z(z) = \int_{-\infty}^{\infty} F_Y(z-x) f_X(x) ~ dx$ ? We have that $F_Z(z)=F_{X+Y}(x+y)=F_{X+Y}(x+(z-x))$, right ? How do we continue to get the above formula ?
21:19
How do you get that formula is a different question. For this question, it suffices that it works. See here - en.wikipedia.org/wiki/Convolution_of_probability_distributions or see here, just after example 5.27 probabilitycourse.com/chapter5/5_2_4_functions.php
That's why we call it convolution - it is operation on two functions that gives a third function. Essentially F_Z(z) = P(Z < z) = P(g(X, Y) < z) and here g(X, Y) = X + Y. Now we write this as P(X + Y < z) = P(Y < z - x)...

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