Jensen inequality, unexplained distribution, very confusing problem

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Discussion Overview

The discussion revolves around the application of Jensen's inequality to a specific problem involving a discrete random variable and the function Y = 1/X. Participants explore the implications of the inequality in the context of the given probability mass function (pmf) and the convexity of the function.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant presents a discrete random variable X with a defined pmf and calculates E[Y] and Y(μX), questioning the application of Jensen's inequality.
  • Another participant points out a potential issue with the function Y = 1/X when X = 0, suggesting it complicates the application of Jensen's inequality.
  • A participant acknowledges the assumption that X > 0 during calculations but still finds the inequality does not hold, likening it to a selection problem.
  • Concerns are raised about the convexity of the function Y = 1/X, with a participant questioning how removing X = 0 affects convexity and whether it could be considered concave.
  • Another participant clarifies that a function can be convex without being differentiable and discusses the implications of non-convexity without confirming concavity.
  • A participant expresses confusion about the implications of setting Y = 0 when X = 0 and its effect on the convexity of the function.

Areas of Agreement / Disagreement

Participants express differing views on the implications of the function Y = 1/X, particularly regarding its convexity and the handling of the case when X = 0. There is no consensus on the resolution of the inequality or the nature of the function.

Contextual Notes

Participants note that the function Y = 1/X is not convex when considering X = 0, which raises questions about the validity of applying Jensen's inequality in this scenario. The discussion highlights the need for careful consideration of the function's properties in relation to the random variable's support.

giglamesh
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Hi everyone
I don't know if I can find someone here to help me understand this issue, but I'll try

the jensen inequality can be found here http://en.wikipedia.org/wiki/Jensen%27s_inequality


I have the following discrete random variable [itex]X[/itex] with the following pmf:

x 0 1 2 3
pr(x) 0.110521 0.359341 0.389447 0.140691

and the summation is (1).

defining the function [itex]Y=\frac{1}{X}[/itex]

Then finding the [itex]\bar{X}=\sum_{i=0}^{3}{iPr(i)=1.560308}[/itex]

Now calculating [itex]E[Y]=E[\frac{1}{X}]=\sum_{i=1}^{3}{\frac{1}{i}Pr(i)}=0.6009615[/itex]
then:
[itex]Y(\bar{X})=\frac{1}{\bar{X}}=\frac{1}{1.560308}=0.640899105[/itex]

According to Jensen inequality what should happen:

[itex]E[\frac{1}{X}] \geq \frac{1}{E[X]}[/itex]
but what I got is the opposite case!:cry:

Now also I have to mention the function [itex]Y=\frac{1}{X}[/itex] is convex given that the second derivative is strictly larger than zero.

Do I misunderstand the Jensen inequality? or is there something wrong with the convixity assumption?
Any idea?
 
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Without having looked at the details of your calculations, you certaintly have a problem with Y = 1/X for X=0
 
Yes you are right to assume it but in the calculation I assumed X>0 when calculating Y.
Even with this assumption I can't approve the opposite inequality.
This can be thought like: choosing one message from X messages problem.

Thanks for reply
 
giglamesh said:
Yes you are right to assume it but in the calculation I assumed X>0 when calculating Y.

Well, you cannot simply "remove" X=0 by setting 1/0=0. Then your function is not convex any more, and this is the source of your problem.
 
thanks winterfors
But why it's not convex
is there a way to prove that it's concave?
Maybe you mean it's non differentiable at X=0

thanks for reply
 
I suggest you look up the definition of a convex/concave function. A function does not have to be differentiable to be convex, for instance is f(x)=abs(x) convex even though is it not differentiable at x=0. Also, that a function is not convex does not imply it is concave, or vice versa.
 
I know dear
that's why I asked about why setting Y=0 when X=0 caused the problem
You said the function is not convex any more, here I didn't get it, does it mean now it's concave?
the second derivative is positive since X>0
Anyway thanks for reply I'll do more research on that
 

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