Solving Fourier Integral for Random Variable Sum

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In summary, the problem involves solving a Fourier integral and using the convolution theorem to find the probability density function of a sum of random variables. The final solution is F(S_n) = (1/2π)*∫-∞ to +∞ [a*b*∫-∞ to +∞ x^(b-1)*exp(-a*x^b)*exp(i*q*x)dx]^n *exp(-i*q*x)dq.
  • #1
capitano_nemo
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Hi to everybody

I have to solve this Fourier integral:

1) f(q)=\int_{-infty}^{+infty}a*b*x^(b-1)*exp(-a*x^b)*exp(i*q*x)*dx

and if S_n=x_1+...+x_n, with S_n the sum of n random variables IID, then I can write:

f_n(q)=[f(q)]^n,(convolution theorem), then the anti-trasform of f_n(q) give the pdf of the variable S_n.

2) F(S_n)=(1/2*pi)*\int_{-infty}^{+infty}f_n(q)*exp(-i*q*x)*dq.

I must to solve the equations 1) and 2) in order to solve my problem, the equation 2) is the final solution of the problem.

Thanks
 
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  • #2
in advance!</code>Thank you for your help. I think I got it now. The solution is as follows:f(q) = a*b*∫-∞ to +∞ x^(b-1)*exp(-a*x^b)*exp(i*q*x)dxf_n(q) = [f(q)]^n (convolution theorem)F(S_n) = (1/2π)*∫-∞ to +∞ f_n(q)*exp(-i*q*x)dqSo, the solution is F(S_n) = (1/2π)*∫-∞ to +∞ [a*b*∫-∞ to +∞ x^(b-1)*exp(-a*x^b)*exp(i*q*x)dx]^n *exp(-i*q*x)dqHope this helps!
 
  • #3
for sharing your question with us. The Fourier integral that you have provided is quite complex and it can be challenging to solve. However, there are a few steps that you can follow to solve it.

Firstly, you can start by using the convolution theorem to simplify the integral. This theorem states that the Fourier transform of the convolution of two functions is equal to the product of their individual Fourier transforms. In your case, this means that you can write f_n(q) as the product of n copies of f(q).

Next, you can use the properties of the Fourier transform to simplify the integral further. For example, the Fourier transform of a product of two functions is equal to the convolution of their individual Fourier transforms. Additionally, the Fourier transform of the exponential function is a delta function. These properties can help you simplify the integral and make it easier to solve.

Once you have simplified the integral, you can use the inverse Fourier transform to get the pdf of the random variable S_n. This involves integrating over all values of q and then taking the inverse Fourier transform of the result.

In order to solve the equations, you may need to use numerical methods or software such as MATLAB or Mathematica. These tools can help you evaluate the integrals and solve the equations to get the final solution.

I hope this helps you in solving your problem. Best of luck!
 

FAQ: Solving Fourier Integral for Random Variable Sum

What is a Fourier Integral?

A Fourier Integral is a mathematical tool used to decompose a function into its constituent frequencies. It allows us to represent a function as a sum of sinusoidal functions with different amplitudes and frequencies.

How is a Fourier Integral used to solve for a random variable sum?

In the context of solving for a random variable sum, a Fourier Integral is used to transform the probability distribution function of the sum into a simpler form, making it easier to calculate the probability of certain outcomes. This is especially useful in cases where the sum of random variables is difficult to calculate directly.

What is the relationship between Fourier Integrals and probability distributions?

The Fourier Integral can be used to transform a probability distribution function into a frequency domain representation. This allows us to analyze the distribution in terms of its constituent frequencies, providing insights into its behavior and properties.

What are some common applications of solving Fourier Integral for random variable sums?

Fourier Integrals are often used in fields such as signal processing, image processing, and finance to analyze and model complex systems. They are also used in probability theory and statistics to solve for the distribution of sums of random variables.

Can Fourier Integrals be solved analytically or numerically?

Fourier Integrals can be solved using both analytical and numerical methods. Analytical solutions involve solving the integral symbolically, while numerical methods involve approximating the integral using numerical techniques. The method used depends on the complexity of the function and the desired level of accuracy.

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