Fourier analysis and prob. distributions?

In summary, it seems that the person is asking if it is possible to create a final probability distribution from several narrower distributions. They state that it would be done through interference or Fourier analysis, but can't explain why it would be a meaningful thing to do.
  • #1
Zaphodx57x
31
0
Ok, this might seem like either a really idiotic question or a really profound one.

Consider a probability distribution. I'm picturing a normal distribution, is it meaningful to be able to build up a final probability distribution from a set of narrower probability distributions?

Ok, that seems like it came out really poorly so i'll say a few of my thoughts. In quantum mechanics we use [tex]\Psi[/tex](r,t) to represent the wave function for very small particles. Then we square this to get |[tex]\Psi(r)|^2[/tex] which is the probability density. This, I believe would then give me a probability distribution. Which in a lot of physics examples is just some multiple of a sine wave. Now, it seems to me(being a novice at both probability and physics) that it may be possible to build up a probability distribution of this sort from several smaller probability distributions through simple interference plotting or Fourier analysis or the like.

However, I can't resolve to myself why this would be a meaningul thing to do. For instance, multiple probability distributions might imply multiple wave functions and hence multiple particles. And multiple particles would interact usually; thus changing the original wave functions and doing something funky.

Can anyone comment on this?
 
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  • #2
I guess profundity is ruled out.
 
  • #3
I have no idea what you intend to do.


You do realize that we can do all sorts of arithmetic on random variables, right? For instance, we can add them, multiply them, square them, divide them, take their logarithm, etc...
 
  • #4
Now, it seems to me(being a novice at both probability and physics) that it may be possible to build up a probability distribution of this sort from several smaller probability distributions through simple interference plotting or Fourier analysis or the like.

However, I can't resolve to myself why this would be a meaningul thing to do. For instance, multiple probability distributions might imply multiple wave functions and hence multiple particles. And multiple particles would interact usually; thus changing the original wave functions and doing something funky.

Indeed. This is just the superposition of wavefunctions. An obvious example is the interference pattern observed in double-slit electron diffraction experiments.
 
  • #5
Thanks for the reply. I'll play with it a little and see what I can get out of it.
 
  • #6
Probability Mixing

You can easily write a single distribution as the sum of two or more. An example is given below. It’s called probability mixing. I suppose you could do the same thing for the magnitude of the wave function.

exp(-pi*x^2)=(1/2)*exp(-pi*x^2)+(1/2)*exp(-pi*x^2)
 

FAQ: Fourier analysis and prob. distributions?

What is Fourier analysis?

Fourier analysis is a mathematical technique used to decompose a complex signal or function into simpler components. It reveals the frequency spectrum of a signal and is commonly used in signal processing, image processing, and other fields of science and engineering.

What is the purpose of Fourier analysis?

The purpose of Fourier analysis is to break down a complex signal or function into simpler components, making it easier to study and understand. It is also used for filtering, noise reduction, and feature extraction in various applications.

What are probability distributions?

Probability distributions are mathematical functions that describe the likelihood of a random variable taking on certain values. They are used to model and analyze random phenomena, such as the outcomes of experiments or the behavior of a system.

How are Fourier analysis and probability distributions related?

Fourier analysis can be used to analyze the frequency spectrum of a probability distribution. This can provide insights into the underlying patterns and characteristics of the distribution, helping to understand and make predictions about the random variable it represents.

What are the most commonly used probability distributions in Fourier analysis?

The most commonly used probability distributions in Fourier analysis are the Gaussian or normal distribution, the exponential distribution, and the uniform distribution. These distributions have specific mathematical properties that make them well-suited for analysis using Fourier techniques.

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