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In mathematics, the limit of a sequence of sets (subsets of a common set ) is a set whose elements are determined by the sequence in either of two equivalent ways: (1) by upper and lower bounds on the sequence that converge monotonically to the same set (analogous to convergence of real-valued sequences) and (2) by convergence of a sequence of indicator functions which are themselves real-valued. As is the case with sequences of other objects, convergence is not necessary or even usual.

More generally, again analogous to real-valued sequences, the less restrictive limit infimum and limit supremum of a set sequence always exist and can be used to determine convergence: the limit exists if the limit infimum and limit supremum are identical. (See below). Such set limits are essential in measure theory and probability.

It is a common misconception that the limits infimum and supremum described here involve sets of accumulation points, that is, sets of where each is in some This is only true if convergence is determined by the discrete metric (that is, if there is such that for all ). This article is restricted to that situation as it is the only one relevant for measure theory and probability. See the examples below. (On the other hand, there are more general topological notions of set convergence that do involve accumulation points under different metrics or topologies.)

Definitions

The two definitions

Suppose that is a sequence of sets. The two equivalent definitions are as follows.

To see the equivalence of the definitions, consider the limit infimum. The use of De Morgan's law below explains why this suffices for the limit supremum. Since indicator functions take only values and if and only if takes value only finitely many times. Equivalently, if and only if there exists such that the element is in for every which is to say if and only if for only finitely many Therefore, is in the if and only if is in all but finitely many For this reason, a shorthand phrase for the limit infimum is " is in all but finitely often", typically expressed by writing " a.b.f.o.".

Similarly, an element is in the limit supremum if, no matter how large is, there exists such that the element is in That is, is in the limit supremum if and only if is in infinitely many For this reason, a shorthand phrase for the limit supremum is " is in infinitely often", typically expressed by writing " i.o.".

To put it another way, the limit infimum consists of elements that "eventually stay forever" (are in each set after some ), while the limit supremum consists of elements that "never leave forever" (are in some set after each ). Or more formally:

    for every       there is a with for all and
for every there is a with for all .

Monotone sequences

The sequence is said to be nonincreasing if for each and nondecreasing if for each In each of these cases the set limit exists. Consider, for example, a nonincreasing sequence Then

From these it follows that
Similarly, if is nondecreasing then

The Cantor set is defined this way.

Properties

Examples

Probability uses

Set limits, particularly the limit infimum and the limit supremum, are essential for probability and measure theory. Such limits are used to calculate (or prove) the probabilities and measures of other, more purposeful, sets. For the following, is a probability space, which means is a σ-algebra of subsets of and is a probability measure defined on that σ-algebra. Sets in the σ-algebra are known as events.

If is a monotone sequence of events in then exists and

Borel–Cantelli lemmas

Main article: Borel–Cantelli lemma

In probability, the two Borel–Cantelli lemmas can be useful for showing that the limsup of a sequence of events has probability equal to 1 or to 0. The statement of the first (original) Borel–Cantelli lemma is

First Borel–Cantelli lemma — If

then

The second Borel–Cantelli lemma is a partial converse:

Second Borel–Cantelli lemma — If

are independent events and
then

Almost sure convergence

One of the most important applications to probability is for demonstrating the almost sure convergence of a sequence of random variables. The event that a sequence of random variables converges to another random variable is formally expressed as It would be a mistake, however, to write this simply as a limsup of events. That is, this is not the event ! Instead, the complement of the event is

Therefore,

See also

References

  1. ^ a b Resnick, Sidney I. (1998). A Probability Path. Boston: Birkhäuser. ISBN 3-7643-4055-X.
  2. ^ Gut, Allan (2013). Probability: A Graduate Course: A Graduate Course. Springer Texts in Statistics. Vol. 75. New York, NY: Springer New York. doi:10.1007/978-1-4614-4708-5. ISBN 978-1-4614-4707-8.