Average distance between random points in 2D

AI Thread Summary
The discussion focuses on calculating the expected mean distance between n random points within a circle of radius a, specifically for n=5. The distance formula between two points in polar coordinates is provided, and the average distance is expressed as a double integral over the area of the circle. The suggestion is made to consider Cartesian coordinates for simplification. The approach involves summing distances between all possible point pairs and normalizing by the area squared. An analytical solution is sought rather than a computational one.
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I'm looking for an analytical solution to a very simple problem I've come across.

Start with a circle of radius a. Now place n points at random positions inside this circle. Can you calculate the expectation value for the mean distance between the points?

For the sake of argument can you solve this for say n=5?

This problem is very simple to solve computationally by just distributing points, measuring the distances and looping many times but I am looking for an analytical solution.
 
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Hi.

Distance d between the two points (r_1,\phi_1) and (r_2,\phi_2) is
$$ d(r_1, r_2, \phi_1, \phi_2 )= \sqrt{{r_1}^2 + {r_2}^2 - 2r_1 r_2 cos(\phi_1- \phi_2) } $$

The average distance is expressed as
$$ \frac{1}{(\pi a^2)^2} \int_0^a r_1 dr_1 \int_0^a r_2 dr_2 \int_0^{2\pi} d\phi_1 \int_0^{2\pi} d\phi_2 d(r_1, r_2, \phi_1, \phi_2 )$$.
 
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It might be simpler to consider cartesian coordinates for this case. The idea would be the same as above.

Basically, you are doing the same thing as you would with any average, but this time it's infinite so you use an integral. Just sum the distance between every possible pair, then divide by the "number of possibilities" (the area squared).
 
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