What Is the Relationship Between the Expected Value of Y and X?

Click For Summary

Discussion Overview

The discussion revolves around the relationship between the expected value of a random variable X and a derived random variable Y, specifically exploring whether the expected value of Y can be expressed in terms of the expected value of X. The context includes theoretical considerations of probability mass functions (PMF) and expected values, as well as the application of Jensen's inequality.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant questions whether the expected value of Y, defined as P(Y) = E[1/(X+1)], can be determined solely from the expected value of X, denoted as \bar{X>.
  • Another participant asserts that knowing the mean of X is insufficient to find the mean of 1/(X+1) without the distribution function of X.
  • It is suggested that if X is strictly positive, Jensen's inequality can be applied to establish bounds on E[1/(X+1)], specifically that 1 >= E[1/(X+1)] >= 1/(E[X]+1).
  • A participant clarifies the notation used, questioning whether Y represents an event and P(Y) its probability.
  • One participant indicates they modified their problem to consider a different distribution, specifically P(Y) = E[1/X], and expresses the need to find the distribution of X to calculate this expected value.
  • Another participant mentions that Jensen's inequality does not apply in their case because the function is defined to be 0 at 0, complicating the convexity consideration.

Areas of Agreement / Disagreement

Participants generally agree that the distribution of X is crucial for determining the expected value of Y, but multiple competing views exist regarding the application of Jensen's inequality and the implications of the PMF being unknown. The discussion remains unresolved regarding the specific relationship between \bar{Y} and \bar{X>.

Contextual Notes

Limitations include the unknown PMF of X, which affects the ability to derive expected values, and the potential issues with applying Jensen's inequality due to the behavior of the function at certain points.

giglamesh
Messages
14
Reaction score
0
Hi all
Sorry for reposting, the previous post wasn't clear enough, it's my mistake, I'll make the question more clear, I found lot of people asking the same question on the Internet.

Given that X is random variable that takes values:

0.....H-1

The PMF of X is unknown, but I can tell what is the expected value which is [itex]\bar{X}[/itex]

There is event Y when calculated it gives the value:

[itex]P(Y)=E[\frac{1}{X+1}][/itex]

The QUESTION: Is there a way to find expected value [itex]\bar{Y}[/itex] in the terms of [itex]\bar{X}[/itex]? regarding that: the PMF of X is unknown we know just the expected value.

It's wrong to say that (just if you can confirm it will be great):
[itex]E[\frac{1}{X+1}]=\frac{1}{E[X]+1}[/itex]
Thanks and sorry for repost
 
Last edited by a moderator:
Physics news on Phys.org
You need the distribution function for X (the mean is not enough) to get the mean of 1/(X+1).
 
thanks apparently I do
 
If X is strictly positive, you can apply Jensen's inequality etc. to get 1 >= E[1/(X+1)] >= 1/(E[X]+1).
 
giglamesh said:
There is event Y when calculated it gives the value:

[itex]P(Y)=E[\frac{1}{X+1}][/itex]

What does that notation mean? Is Y some event ( like "A red bird lights on the window") and P(Y) is its probability?
 
hi giglamesh, have you had the answer so far? I am having exactly problem like you
 
hi all
yes P(Y) is another event which probability is the expected value of other function of random variable.
Applying Jenesen Inequality does not help because it gives the lower bound.
So I decided to work on the problem to get X distribution to calculate the E[1/(1+X)]

but few days later I modified the problem to another distribution P(Y)=E[1/X] in another post.
Greetings
 
did you get the answer for E(1/X) as well?
 
Last edited by a moderator:
yes just find the distribution of X, the PMF (discret case)
then calulate the probability like this:
P(Y)=E[1/X]=sum_{i=1}^{i=n}{(1/i)*P(X=i)}
using Jenesen inequality here doesn't help because the funtion is defined to be 0 at 0 so we can't consider it convex.
hope that would help
 

Similar threads

  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 14 ·
Replies
14
Views
2K
  • · Replies 2 ·
Replies
2
Views
4K
  • · Replies 76 ·
3
Replies
76
Views
7K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 17 ·
Replies
17
Views
3K
  • · Replies 8 ·
Replies
8
Views
2K
  • · Replies 9 ·
Replies
9
Views
3K
  • · Replies 2 ·
Replies
2
Views
5K