Expected values in Probability space

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SUMMARY

The discussion centers on the relationship between the limit inferior of a sequence of random variables and their expected values within a probability space defined as (Ω, ε, P). The user seeks an example where I_{p}(lim inf_{n -> ∞}X_{n}) is less than lim inf_{n -> ∞}I_{p}(X_{n}). The proposed solution involves constructing a nonconvergent sequence of random variables, specifically X_n, where E[X_n] remains constant. The example provided uses Ω = N and P(ω) = 2^{-ω}, with X_n(ω) = 2^nδ_{ω,n}, illustrating the nonconvergence and the evaluation of integrals related to the limit inferior.

PREREQUISITES
  • Understanding of probability spaces, specifically (Ω, ε, P).
  • Familiarity with limit inferior concepts in the context of sequences.
  • Knowledge of expected values and their calculations in probability theory.
  • Experience with the dominated convergence theorem and its implications.
NEXT STEPS
  • Study the properties of limit inferior in sequences of random variables.
  • Explore the dominated convergence theorem and its applications in probability.
  • Learn about the evaluation of integrals involving random variables in probability spaces.
  • Investigate examples of nonconvergent sequences in probability theory.
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Students and professionals in mathematics, particularly those focusing on probability theory, stochastic processes, and statistical analysis, will benefit from this discussion.

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Homework Statement



Let a probability space be (Ω, \epsilon, P). A set of random variables X1,...,Xn

Give an example where I_{p}(lim inf_{n -&gt; ∞}X_{n}) < lim inf_{n -&gt; ∞}I_{p}(X_{n})


The attempt at a solution

I know that I_{p}(lim inf_{n -&gt; ∞}X_{n})=E[lim inf_{n -&gt; ∞}X_{n}]
and lim inf_{n -&gt; ∞}I_{p}(X_{n}) = lim inf_{n -&gt; ∞}E[X_{n}]

I think I need to find a sequence of Xn such that lim inf Xn will have a smaller value than all the individual expected value, E[Xn].

Am I on the correct path? I'm kind of stuck here and not sure how to proceed.

Would be really really thankful for the help.
 
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How is ##\displaystyle \liminf_{n \to \infty} X_n## defined? The limit of the "smallest value" of those random variables?
In that case, I don't see how both can be equal apart from trivial Xi.
 
Equality actually holds for surprisingly many cases. See for example, the dominated convergence theorem. So you'll need to look at sequences of random variables which fail that theorem.
Without giving away too much, try to make a nonconvergent sequence ##X_n## such that ##E[X_n]## are all constant.
 
Okay, then I don't understand how to evaluate ##\displaystyle \liminf_{n \to \infty} X_n##.
 
If ##X = \liminf_n X_n##, and ##\omega## is an outcome, then it is defined by
X(\omega) = \liminf_n X_n(\omega)

So it's just the pointswise inferior limit.
 
micromass said:
try to make a nonconvergent sequence ##X_n## such that ##E[X_n]## are all constant.

I saw this example...

Ω=N
P(ω)=2, ω in Ω
Xn(ω)=2nδω,n

I can see that Xn is not convergent.

But I'm not quite sure in computing the integrals... I'm very bad with the lim inf concept >_< So, I have written my attempt on evaluating the integrals and reasoning that I've used.

My attempt,
lim inf Xn
=lim inf [2nδω,n]
Since this takes in values 0 or 2n
=0

E[Xn]
=IP[2nδω,n]
= ∫2nδω,n2
When ω=n, this reduces to ∫δω,n
ω,n

I'm not very sure about the way I evaluate the integral. Would be very helpful for some guidance.
 

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