Expected Value of reciprocal (Sorry for reposting)

In summary, the conversation discusses finding the expected value of Y in terms of the expected value of X, given that the PMF of X is unknown. Various methods are suggested, including using Jensen's inequality and calculating the distribution of X. Ultimately, it is concluded that finding the distribution of X is necessary in order to calculate the expected value of Y.
  • #1
giglamesh
14
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
 
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  • #2
You need the distribution function for X (the mean is not enough) to get the mean of 1/(X+1).
 
  • #3
thanks apparently I do
 
  • #4
If X is strictly positive, you can apply Jensen's inequality etc. to get 1 >= E[1/(X+1)] >= 1/(E[X]+1).
 
  • #5
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?
 
  • #6
hi giglamesh, have you had the answer so far? I am having exactly problem like you
 
  • #7
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
 
  • #8
did you get the answer for E(1/X) as well?
 
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  • #9
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
 

What is the expected value of a reciprocal?

The expected value of a reciprocal is the sum of the products of all possible values and their corresponding probabilities. In other words, it is the average value that we can expect to get when taking the reciprocal of a random variable.

Why is the expected value of a reciprocal important?

The expected value of a reciprocal is an important concept in probability and statistics because it helps us understand the average behavior of a random variable. It can also be used to make predictions and decisions in various fields, such as finance, economics, and engineering.

How do you calculate the expected value of a reciprocal?

To calculate the expected value of a reciprocal, we multiply each possible value by its corresponding probability and then add all these products together. This can be represented mathematically as E(X) = ∑ (x * P(x)), where x is the possible value and P(x) is its probability.

What is the difference between expected value and average value?

The expected value of a reciprocal is often referred to as the average value, but they are not exactly the same. The expected value takes into account the probabilities of different outcomes, while the average value is simply the sum of all values divided by the number of values. In other words, the expected value is a weighted average.

How can the expected value of a reciprocal be used in decision making?

The expected value of a reciprocal can be used in decision making by comparing it to a certain threshold or target value. If the expected value is greater than the threshold, it may be a favorable decision to take the reciprocal of the random variable. However, if the expected value is less than the threshold, it may be better to avoid taking the reciprocal. This can help in minimizing risks and maximizing potential gains.

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