Characteristic function in prbability

In summary, the conversation was about the characteristic function and its integral. The person mentioned trying to integrate the expression by parts and getting a similar term to the left hand side, but with a different coefficient. However, the evaluated integral was infinite, which contradicts with the information from the provided link. The conversation also touched on using LaTeX math language to show the steps, but the person was not familiar with it.
  • #1
O.J.
199
0
I was reading about it here:
http://mathworld.wolfram.com/CharacteristicFunction.html
very neat. But then I tried out of boredom integrating the expression by parts where u = the exponential term and v = f (x) (or P(x)). The integral came out nicely as I got a term similar to the left hand side except with a different coefficient. Anyway, the evaluated integral was infinite, which contradicts with this link. Is there something wrong with my logic? Can any of you try evaluating the expression by integration by parts and show ur results? thank u
 
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  • #2
I'm not sure what integral you are evaluating. From what I see a Taylor series expansion is done on the exponential function and the moments come out by definition. That said. I didn't really read the link.
 
  • #3
The integral I am talking about is the integral that defines the characteristic function. the definition.
 
  • #4
O.J. said:
The integral I am talking about is the integral that defines the characteristic function. the definition.

Show your steps. I think my previous comment still applies.
 
  • #5
I don't know how to use that Latex math language :(...
 
  • #6
O.J. said:
I don't know how to use that Latex math language :(...

You should learn, it is easy.
 

Related to Characteristic function in prbability

1. What is a characteristic function?

A characteristic function is a mathematical function that uniquely describes a probability distribution. It is used to represent the probability distribution of a random variable as a complex-valued function.

2. How is a characteristic function different from a probability density function?

A probability density function describes the probability distribution of a continuous random variable, while a characteristic function describes the probability distribution of both continuous and discrete random variables.

3. What are the properties of a characteristic function?

The properties of a characteristic function include: (1) symmetry, (2) continuity, (3) boundedness, (4) monotonicity, and (5) positivity. These properties ensure that the characteristic function is a valid representation of a probability distribution.

4. How is a characteristic function used in probability and statistics?

A characteristic function is used to calculate the moments of a probability distribution, such as the mean, variance, and higher order moments. It is also used to find the distribution of a sum of independent random variables.

5. Can a characteristic function uniquely determine a probability distribution?

Yes, a characteristic function uniquely determines a probability distribution. This is known as the inversion theorem, which states that a probability distribution can be uniquely determined by its characteristic function.

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