How Can You Determine the Characteristic Function of Joint PDFs?

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Discussion Overview

The discussion revolves around the determination of the characteristic function of joint probability density functions (PDFs), particularly in the context of independent random variables. Participants explore the rules and methods for deriving the characteristic function when given individual characteristic functions of independent components, such as waiting time and jump densities.

Discussion Character

  • Exploratory
  • Technical explanation
  • Mathematical reasoning

Main Points Raised

  • One participant inquires about sources and rules for determining the characteristic function of a joint PDF, specifically for independent waiting time and jump densities.
  • Another participant references Wikipedia, stating that for independent random variables, the joint characteristic function is the product of the individual characteristic functions.
  • A different participant clarifies that while the product rule applies to independent random variables, they are specifically interested in the joint PDF and provides an example involving a particle's waiting time and jump distribution.
  • Another participant suggests a book by Lukacs on characteristic functions and provides a mathematical expression for the characteristic function of the joint PDF based on the individual distributions.
  • One participant expresses gratitude for the reference provided, indicating its usefulness.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the methods for determining the characteristic function of joint PDFs, as there are differing focuses on the joint PDF versus the product of individual characteristic functions. The discussion remains unresolved regarding the most elegant approach to the problem.

Contextual Notes

Participants express varying assumptions about the independence of random variables and the specific forms of the joint PDF, which may affect the applicability of the discussed methods.

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Hi.
Does anyone know a good source for learning about the characteristic function of a joint pdf. Is there any nice rules for that? For example assume having a waiting time density and a jump density which are independent (easy things first). Is there an elegant way to get the characteristic function of the process if I have the two individual cf's?
Thanks
 
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emptyset said:
Hi.
Does anyone know a good source for learning about the characteristic function of a joint pdf. Is there any nice rules for that? For example assume having a waiting time density and a jump density which are independent (easy things first). Is there an elegant way to get the characteristic function of the process if I have the two individual cf's?
Thanks

According to wikipedia for independent random variables the joint characteristic function is just the product of the characteristic function for each random variable.

http://en.wikipedia.org/wiki/Charac..._theory)#Basic_manipulations_of_distributions
 
Thanks for your reply. This is true for a sequence of independent (and not necessarily identically distributed) random variables. However, I am looking for the joint pdf.
As an example, assume you have a particle sitting around at x' for a random time tau with the distribution y(t) and then jumping to x'' with the jump distribution z(x).
I am looking for a nice way of dealing with the characteristic funtion of the joint pdf v(x,t)=y(t)z(x)
given I now the charactersitic functions of y and z.
 
A good book on characteristic functions is by Lukacs (called characteristic functions).

In your example if you have v(x,t)=y(t)z(x), then the cf would be
\psi(\theta)=\int_{\mathbb{R}}\int_{\mathbb{R}}e^{i\theta x t}z(x)y(t) dx dt =\int_{\mathbb{R}}\psi_z(t\theta)y(t) dt
 
thanks a lot for the information. that reference will be usefull.
 

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