PDF of random motion - similar to Browninan motion

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The discussion revolves around calculating the probability density function (PDF) of a particle's distance from the origin after N random movements in a 2-D space, akin to Brownian motion. The user initially solved a simpler problem involving a frog's jumps and later extended the inquiry to a more complex scenario involving uniform probability density for random directions. The approach includes defining angles and using trigonometric identities to express the final position, but the user struggles with calculating the PDF of the derived variables. The suggestion to utilize the central limit theorem is proposed as a potential solution.

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jaumzaum
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Hello guys, and sorry for my english in advance.

I was presented some time ago with the following problem:
Suppose there is a frog that jumps in any direction randomly, and all the jumps have size 1. What's the probability of, after 3 jumps, the frog be less than 1 unit from the origin.

I solved the problem with a double integral (if I remember well the answer is 25%), but then I thought about a similar and more general problem that I found out it's a lot similar to the Browninan motion.

Suppose there is a particle in a 2-D space that moves only in displacements of size 1 and random directions that follow a uniform probability density. What is the PDF of the particle distance from the origin after N moves? What is the probability of, after N movements, the particle end in a distance less then kN from the origin? N>>1


I approached it in the following way (and I couldn't finish):Let ##\theta_i ∈ [0, 2\pi)##
Final position: ## (∑cos(\theta_i), ∑sin(\theta_i))##
So ##(∑cos(\theta_i))^2 + (∑sin(\theta_i))^2 < k^2N^2##
Define ##\theta_{ij} = \theta_i - \theta_j ∈ (-2\pi, 2\pi)##
##∑cos(\theta_{ij})) < (k^2 N^2 - N)/2##

The PDF of ##\theta_ij## is not uniform, but if we define
##\alpha_{ij} =
\begin{cases}
x & \text{if } 0 \leq x < \pi \\
2\pi - x & \text{if } \pi \leq x < 2\pi \\
\end{cases}##

Then ##\alpha_{ij}## has a uniform distribution and covers all the values of ##cos(x)##, that way we can define ##C_{ij} = cos(\alpha_{ij})## with probbility density function ##1/sqrt(1-x^2)## and

##∑C_{ij} < (k^2N^2-N)/2 ##

but I do not find a way to calulate the PDF of the N (N-1)/2 variables above.

Can anyone help me?
 
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I suggest using the central limit theorem.
 

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