An obvious remark:
If a sequence of independent random variables converge almost surely to some limit
, this limit must be a constant (almost surely).
I’ve been thinking about the Central Limit Theorem about related Large Deviations results this afternoon, and wasted almost an hour worrying about situations which were effectively well-disguised special cases of the above.
Why is it true? Well, suppose each is
-measurable. But by independence, we might as well take
. Then the limit variable
is independent of
for all
, and thus independent of
. If
is independent of itself, it must be almost surely constant.
Related articles
- Weak Law of Large Numbers and Central Limit Theorem via the Levy’s continuity theorem. (maikolsolis.wordpress.com)
- republicans reject the central limit theorem? (orgtheory.wordpress.com)
- Poisson processes appropriate for today (gottwurfelt.wordpress.com)
Yes. This is a simple consequence of the Kolmogorov 0-1 law as the event {X > x} is in a tail event. The proof of the 0-1 law is nearly identical to the one given here.