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Supposing I have four independent variables $A, B, C$ and $D$, all of which are uniformly distributed from $-1$ to $1$:

  • How can I find the probability of both $$ A + D < 0\quad\mbox{and}\quad \frac{\left(A + D\right)^{2}}{4} > AD - BC\quad \mbox{happening}\ ? $$
  • I understand that $\operatorname{P}\left(A + D < 0\right) = 1/2$ since it is just the proportion of area of the lower triangle of the $\left[-1, 1\right]\times\left[-1, 1\right]$ square to the whole square, but how do I set up the $\left(~\mbox{quadruple}\,?~\right)$ integral to find the probability of the second event $?$.
  • In general for an event depending on four variables, we have $\int\limits_{R} \operatorname{f}\left(a, b, c, d\right)\,{\rm d}R$ where $R$ is the hyper-region we are interested in and $\operatorname{f}\left(a, b, c, d\right)$ is the probability density function, but I'm not sure how to set up each of those.

Would I multiply the two probabilities afterwards $?$

I'm quite green when it comes to probability so apologies if the question is obvious.

  • $$\text{ClearAll}[a,b,c,d];\frac{\int _{-1}^1\int _{-1}^1\int _{-1}^1\int _{-1}^1\text{If}\left[a+d<0\land \frac{1}{4} (a+d)^2>a d-b c,1,0\right]{\rm d}a,{\rm d}b,{\rm d}c,{\rm d}d}{2^4}$$ $$ \frac{1323+162 \log (2)-178 \log (3)+8 \log (9)-324 \log \left(\frac{3}{2}\right)+972 \operatorname{arccoth}(5)}{3888} \approx 0.3403 $$ – Felix Marin Oct 17 '21 at 02:00
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    Thank you. I now see this is the closest I'll get to an answer, since it's one of those problems where not much can reasonably be done by hand. If you put this as an answer instead of a comment, I'll accept it. – use4829303 Oct 17 '21 at 07:35
  • @FelixMarin How did you get a computer to compute it into logarithm form? – Davis Parks Oct 18 '21 at 16:38

2 Answers2

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I don't have time to work it out, but here's my initial thought. The inequality $\frac{(A+D)^{2}}{4} > AD - BC$ is the same as $-\left(\frac{A-D}{2}\right)^{2} <BC$. I would set $X=-\left(\frac{A-D}{2}\right)^{2}$ so that $X \in [-1,0]$ and solve for $f_{X}(x)$. Then, you can solve for $f_{B,C}(b,c \mid x)$ and solve the problem with $$\int\int\int f_{B,C}(b,c \mid x) f_{X}(x)\ dx\ db\ dc.$$

I don't envy you. This problem is a pain in the butt, unless there's some nifty trick that I'm missing.

  • How do I find $f$? Is it just multiplying the uniform probability functions of the four variables $A, B, C$ and $D$ since they are independent? – use4829303 Oct 17 '21 at 01:12
  • Well, $f_{X}(x)$ is the derivative of $P(x< - \left(\frac{A-D}{2}\right)^{2})$. This is a really complicated problem. – Davis Parks Oct 17 '21 at 01:24
  • Is conditional probability involved here? $A, B, C$, and$ D$ being independent doesn't come into play? – use4829303 Oct 17 '21 at 01:29
  • The answer is something around $0.3402$, so it's most likely not going to be a clean answer like you're probably hoping. – Davis Parks Oct 17 '21 at 01:29
  • I wasn't expecting a clean answer. I mostly just want to know how to set up the integral. If I then have to evaluate it numerically using software, that's fine. – use4829303 Oct 17 '21 at 01:31
  • Anyway, it's a cumbersome algebraic procedure. – Felix Marin Oct 17 '21 at 01:37
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    Which is the complicated part? Finding the limits or the probability density function? – use4829303 Oct 17 '21 at 01:39
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This can be done by hand (although I don't know about the qualifier "reasonably").

As done in the other answer (slightly modified), we can let $X = \frac{A-D}{2}$, which since $A$ and $D$ are both $\sim U(-1,1)$ has a triangular distribution centered at $0$ and extending from $-1$ to $1$. If we consider $|X|$, this has a CDF of $P(|X|<x) = 2x-x^2$ for $x \in (0,1)$. Since $|X|$ is positive, we have $P(X^2 < x) = P(|X|<\sqrt{x}) = 2\sqrt{x} - x$. Differentiating gives the PDF $f_{X^2}(x)=\frac{1}{\sqrt{x}} - 1$, for $x \in [0,1]$.

From symmetry, we can see from the joint distribution of $A$ and $D$ that the distribution of $X$ when $A+D>0$ is the same as the distribution of $X$ when $A+D<0$, and as stated in the post, these are equally likely, producing a factor of 1/2.

We can also define $Y=-BC$. If $Y < 0$, then clearly $X^2 > Y$. We have $P(Y<0)=\frac{1}{2}$, again by symmetry.

If $Y\ge 0$ (which occurs half the time), then we want to know if $X^2>|Y|=|B||C|$, which is the product of two independent rv $\sim U(0,1)$, so $|Y|$ has the PDF $f_{|Y|}(y) = -\log(y)$ for $ y \in (0,1)$.

see product distribution of two uniform distribution, what about 3 or more

Since $X^2$ and $|Y|$ are independent, their joint distribution is the product of their distributions, so we want to integrate:

$$\frac{1}{2}\left[ \frac{1}{2} + \frac{1}{2} \int_{y=0}^{y=1} \int_{x=y}^{x=1}\left(\frac{1}{\sqrt{x}} - 1\right)\left(-\log(y)\right)dx\, dy \right]$$

Where the first $\frac{1}{2}$ is from the factor for $A+D>0$, the next $\frac{1}{2}$ is for when $Y<0$, and the next $\frac{1}{2}$ is from the factor for $Y>0$.

For the inner integral: $$\int_{x=y}^{x=1}\left(\frac{1}{\sqrt{x}} - 1\right) dx= (1+y-2\sqrt{y})$$

For the outer integral: $$\int_{y=0}^{y=1} (1+y-2\sqrt{y})\left(-\log(y)\right)dy$$

Letting $u = \sqrt{y}$, so that $dy = 2u \,du$: \begin{align} \int_{u=0}^{u=1} (1+u^2-2u)\left(-2\log(u)\right)2u\, du &= -4 \int_{u=0}^{u=1} (u-2u^2+u^3)\left(\log(u)\right) du \\ &= -4 \left(\int_{u=0}^{u=1} u\log(u) du - 2\int_{u=0}^{u=1} u^2\log(u) du + \int_{u=0}^{u=1} u^3\log(u) du \right) \\ &=4 \left( \frac{1}{4} - \frac{2}{9} + \frac{1}{16} \right) \\ &=1-\frac{8}{9}+\frac{1}{4} \\ &=\frac{13}{36} \\ \end{align}

For the integrals, I looked up in a table that for $m \ge 0$: $$\int x^m \log(x)dx = \frac{x^{m+1}}{m+1} \left( \log(x) - \frac{1}{m+1} \right)$$

Putting this in the main equation gives the probability:

$$\frac{1}{2}\left[ \frac{1}{2} + \frac{1}{2} \frac{13}{36} \right] = \boxed{ \frac{49}{144} } \approx 0.3403$$

Joe
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