Validity of replacing X by E[X] in a formula

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Discussion Overview

The discussion revolves around the validity of replacing a random variable in the denominator of a fraction with its expected value in the context of Monte Carlo simulations. Participants explore whether this result can be generalized and seek related results from analysis or probability theory.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant proposes a generalization of a theorem regarding the replacement of a random variable by its expected value under certain conditions involving sequences of random variables.
  • Another participant notes that when two random variables are independent, the expectation of their ratio does not equal the ratio of their expectations, highlighting that E(1/B) does not equal 1/E(B) in general.
  • A later reply provides a specific example illustrating the inequality between E[1/B] and 1/E[B], suggesting that under certain constraints, this inequality may hold as an equality in the limit.
  • Another participant comments on the condition involving the ratio of the standard deviation of V_k to its expected value, suggesting it may lead to a constant distribution if E(V_k) is bounded.

Areas of Agreement / Disagreement

Participants express differing views on the conditions under which the replacement of a random variable by its expected value is valid, indicating that multiple competing perspectives remain without a clear consensus.

Contextual Notes

Participants acknowledge that additional premises may be necessary for the proposed generalization to hold, and there is uncertainty regarding the specific constraints that would allow for the equality in the limit.

andrewkirk
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Hello all. I am working on proving some theorems about Monte Carlo simulation and have proven a theorem that, in a certain formula, it is valid to replace a random variable in the denominator of a fraction by its expected value. I have been wondering whether this result can be generalised to obtain wider application.

A nice generalisation of the theorem would be as follows:

If ##(U_k)_{k\in \mathbb N}## and ##(V_k)_{k\in \mathbb N}## are sequences of random variables, not necessarily independent, and ##\lim_{k\to\infty}\frac{\sqrt{\mathrm{Var}(V_k)}}{E[V_k]}=0##, then
$$\lim_{k\to\infty}E\left[\frac{U_k}{V_k}\right]=\lim_{k\to\infty}\frac{E\left[U_k\right]}{E\left[V_k\right]}$$
provided the limit on the RHS exists. (##k## is the number of Monte Carlo trials)

Before setting out to try to work out whether this is correct and, if so, to prove it, I'd like to first check if anybody knows of any similar results from analysis or probability theory. While it would be fun to prove it from scratch, it's a bit peripheral to what I'm doing so, if there's a known result that validates it, it would be better to just use that.

There may be some additional premises needed in order to make it work.

Thank you in advance for any suggestions.
 
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When A and B are independent, E(A/B)=E(A)E(1/B). In general E(1/B)≠1/E(B).
 
mathman said:
When A and B are independent, E(A/B)=E(A)E(1/B). In general E(1/B)≠1/E(B).
Yes, that's part of the process I went through. A very simple example is when B has value 1 or 2, each with probability 50%. Then E[1/B]=3/4 and 1/E[ B ]=2/3. However, under certain constraints like those above (and maybe a few more - part of the problem is to work out which ones) that inequality can become an equality in the limit as ##k\to\infty##. In general, the numerator and denominator will not be independent, so we can't necessarily factor the expectation.
 
The condition \frac{\sigma (V_k)}{E(V_k)} -> 0 seems to lead to a constant distribution, as long as E(V_k) is bounded. I suspect this is what you are looking for.
 

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