Difference in random walk description

In summary, the conversation discusses the concept of random walks and how they relate to particle movement. It is noted that the solution to the differential equation starting from a delta is a gaussian, which can result in the particle moving at different speeds than dx in dt. The central limit theorem is mentioned as a way to explain this phenomenon, but there is a concern about the possibility of infinite speed. The conversation ends with a discussion about methods to avoid this.
  • #1
jk22
729
24
We studied random walk starting from a probability conservation : p(x,t)=p(x+dx,t+dt)+p(x-dx,t+dt)

Which means the particle can go left or right by dx in time dt.

The solution of the differential equation starting from a delta is a gaussian, which means the particle could go apparently at different speed than dx in dt.

Where does this difference comes from ?
 
Physics news on Phys.org
  • #2
Gaussian results from the fact that you adding the results of an "infinite" number of "infinitesimal" steps, which are independent and where each step can be + or -. Essentially it is the result of the central limit theorem applied to the limit of a binomial distribution.
 
  • #3
So after a time dt it could be that we added an infinite number of + hence reaching infinity ?

What could we do if we want a maximal speed ? So that it becomes compatible with relativity.
 
  • #4
jk22 said:
So after a time dt it could be that we added an infinite number of + hence reaching infinity ?

What could we do if we want a maximal speed ? So that it becomes compatible with relativity.
Each step (+ or -dx) takes time dt. The central limit theorem describes the result of integrating over time. The distance traveled in time T has a Gaussian distribution with variance proportional to T.

I don't understand what you are trying to with infinity. If all the steps are in the same direction, it is not a random walk.
 
  • #5
Adding only plus is a limit case, when the time is small paths going around the origin are preferred but then this difference gets smaller. but what i mean is that the speed at time dt can reach infinity. And i look for a method to avoid this.
 
  • #6
jk22 said:
Adding only plus is a limit case, when the time is small paths going around the origin are preferred but then this difference gets smaller. but what i mean is that the speed at time dt can reach infinity. And i look for a method to avoid this.
What do you mean by "small paths going around the origin"? I thought you were describing a 1 dimensional case.
 
  • #7
It is rather through or at the origin.
 
  • #8
The basic point is that each tiny step can be + or -. To get infinite speed you need a preponderance of one or the other.
 

Related to Difference in random walk description

1. What is a random walk?

A random walk is a mathematical concept that describes the path of a randomly moving object. It is a model often used in the study of probability and statistics.

2. How is a random walk described?

A random walk is typically described as a sequence of steps taken by an object in a random fashion. These steps can be taken in any direction and can have varying lengths.

3. What is the difference between a random walk and a biased walk?

A random walk is a walk where each step has an equal probability of occurring and is independent of the previous steps. A biased walk, on the other hand, is a walk where the probability of taking a step in a certain direction is not equal, and can be influenced by previous steps or external factors.

4. How is a random walk useful in science?

Random walks are used in science to model and analyze various processes and phenomena, such as the movement of particles in a fluid, the spread of diseases, and the behavior of stock prices. They also have applications in physics, biology, and economics.

5. What are some real-world examples of random walks?

Random walks can be seen in various real-world scenarios, such as the movement of molecules in a gas, the flight pattern of birds, the movement of stock prices in financial markets, and the behavior of crowds in public spaces.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
221
Replies
4
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
0
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
6
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
5
Views
922
  • Set Theory, Logic, Probability, Statistics
Replies
2
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
19
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
8
Views
1K
Back
Top