Integral of the Square of density probability function

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Discussion Overview

The discussion revolves around the integral of the square of a probability density function (PDF) over a bounded interval, specifically focusing on the implications of this integral in the context of probability spaces and norms.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks the value of the integral of the square of a density probability function on a bounded interval.
  • Another participant suggests that the value could vary based on the specific density function used.
  • A question is raised about how to characterize the variability of this integral and its relation to norms in probability spaces.
  • It is mentioned that the integral of the squared PDF can be interpreted as a measure of the average probability density, with a claim that it is greater than or equal to the average probability density of a constant PDF.
  • There is a clarification that the initial assumption of a joint density function may not apply if considering a univariate density, leading to a different expression for the integral.
  • One participant expresses confusion regarding the distinction between L1 and L2 norms in the context of probability spaces.
  • Another participant argues against using the L2 norm, stating that the integral can take any value depending on the choice of the PDF, which is arbitrary.
  • There is a reiteration of the confusion regarding why the L1 norm is preferred over the L2 norm in this context.
  • One participant emphasizes the need to take the square root to obtain the L2 norm, while another provides an example illustrating the variability of the integral based on the chosen PDF.

Areas of Agreement / Disagreement

Participants express differing views on the appropriate norm to use in this context, with some advocating for the L1 norm and others suggesting the L2 norm. The discussion remains unresolved regarding the characterization of the integral and its implications.

Contextual Notes

The discussion highlights the dependence on the choice of the probability density function and the implications of using different norms, but does not resolve the mathematical steps or definitions involved.

BoMa
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Hi,
I'm looking for the value of the integral of the square of a density probability function
on a bounded interval.

Tks
 
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It could be anything, depending on the density function itself.
 
Is there a way to characterise this "anything" , you're talking about.

[tex]\int \int f^{2}(x,y) dx\,dy[/tex]

Some norm on the probability space ?
 
You wouldn't use it as a norm (the L^2 norm), having a probability space implies that you are using the L^1 norm (and that this norm equals one).

The integral of the squared PDF can however be interpreted as a measure of the average probability density of the PDF, i.e. how concentrated the probability is. Further more, you know that it will be larger or equal to the average probability density of a constant PDF:

[tex]\int \int f^{2}(x,y) dx\,dy \geq 1 / \int \int dx\,dy[/tex]
 
Just a question: you seem to be automatically assuming that the density is a joint density of two variables, hence you write

[tex] \iint f^2(x,y) \, dxdy[/tex]

If, however, you consider a univariate density, the proper expression for the integral of the density squared is

[tex] \int f^2(x) \, dx[/tex]
 
Yes I'm talking about the bivariate bounded probability density function (pdf) [tex]f(x,y).[/tex]

Sorry I can't understand the difference between L1 or L2 norm on the probability space.

About not using the L2 norm , I thought that the pdf could be written as
[tex]\int^{b}_{a} \int^{b}_{a} f^{2}(x,y)dxdy =\int^{b}_{a} \int^{b}_{a} |f^{2}(x,y)|dxdy=||f||^2[/tex]
which looks like the L2 norm , more than the L1 norm.

Could you please explain in more details why this case can't be seen as L2 norm ?
 
what are L1 and L2?
 
BoMa said:
Yes I'm talking about the bivariate bounded probability density function (pdf) [tex]f(x,y).[/tex]

Sorry I can't understand the difference between L1 or L2 norm on the probability space.

About not using the L2 norm , I thought that the pdf could be written as
[tex]\int^{b}_{a} \int^{b}_{a} f^{2}(x,y)dxdy =\int^{b}_{a} \int^{b}_{a} |f^{2}(x,y)|dxdy=||f||^2[/tex]
which looks like the L2 norm , more than the L1 norm.

Could you please explain in more details why this case can't be seen as L2 norm ?

To get the L2 norm, take a square root.

I really don't understand what you are trying to achieve. To give a simple example. Assume that f is constant over a square with area 1/A, then f=A over this area and the integral of f2 is A. Since A is completely arbitrary, the integral can have any value.
 
I understood that the value will be any constant depending on the choice of f which is arbitry chosen here. So I wanted to say that it is some L2 norm. But someone on the list said that It should be L1 norm (not L2 norm !), because the problem here is on a probabity space. So I wonder why, he said L1 norm instead of L2 norm ?
 
  • #10
BoMa said:
I understood that the value will be any constant depending on the choice of f which is arbitry chosen here. So I wanted to say that it is some L2 norm. But someone on the list said that It should be L1 norm (not L2 norm !), because the problem here is on a probabity space. So I wonder why, he said L1 norm instead of L2 norm ?

Sorry, I am not a mind reader. Just make sure you take the square root to get the norm. In the simple example the L2 norm is √A.
 

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