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A probability provides a quantitative description of the likely occurrence of a particular event.

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Compute $P\left(\int_0^1W(t)dt>\frac{2}{\sqrt3}\right)$ where $W(t)$ is a Wiener process

Now you can compute your probability using the standard normal CDF $\Phi$: $$\begin{aligned} & P\left(\int_0^1 W_s ds > \frac{2}{\sqrt{3}}\right)\\ & = P\left(Z \frac{1}{\sqrt{3}}> \frac{2}{\sqrt{3}}\right …
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2 votes

How to prove this identity assuming Gaussian distribution?

Since $Y-F$ and $F$ are jointly gaussian, then $Y = (Y-F) + F$ is also gaussian. You can compute the moments from the moments of $Y-F$ and $F$, or using the tower property: For the mean: $$\mathbb{ …
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