PDF of random motion - similar to Browninan motion

In summary, the conversation discusses a problem involving a frog jumping randomly and the probability of its distance from the origin after 3 jumps. The problem is then expanded to a 2-D scenario with a particle moving in random directions. The approach involves defining variables and using the central limit theorem, but the PDF of the variables cannot be calculated.
  • #1
jaumzaum
434
33
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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  • #2
I suggest using the central limit theorem.
 

1) What is a PDF of random motion?

A PDF (Probability Density Function) of random motion is a mathematical function that describes the probability of a particle undergoing random motion within a given space. It is used to analyze the distribution of particles and their movements over time.

2) How is a PDF of random motion related to Brownian motion?

Brownian motion is a type of random motion in which particles move in a random fashion due to collisions with smaller particles. The PDF of random motion can be used to analyze and understand the behavior of particles in Brownian motion.

3) What factors affect the shape of a PDF of random motion?

The shape of a PDF of random motion can be affected by various factors, such as the size and shape of the particles, the temperature of the environment, and the presence of external forces such as gravity or electric fields. These factors can influence the speed and direction of the particles' movements, thus affecting the overall shape of the PDF.

4) How is the PDF of random motion calculated?

The PDF of random motion is typically calculated using mathematical equations and statistical methods, such as the Boltzmann distribution or the Fick's law of diffusion. These equations take into account various parameters, such as the particle concentration, diffusion coefficient, and temperature, to determine the probability of a particle's position and movement in a given space.

5) What are some real-world applications of studying the PDF of random motion?

The PDF of random motion has many practical applications in fields such as physics, chemistry, and biology. It is used to understand and predict the behavior of particles in various systems, such as the diffusion of molecules in a liquid, the movement of cells in a biological tissue, or the spread of pollutants in the atmosphere. It also plays a crucial role in the development of technologies such as drug delivery systems, filtration processes, and microfluidic devices.

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