In
probability theory, the Borel–Cantelli
lemma is a
theorem about
sequences of
events. In general, it is a result in
measure theory. It is named after
Émile Borel and
Francesco Paolo Cantelli, who gave statement to the lemma in the first decades of the 20th century.[1][2] A related result, sometimes called the second Borel–Cantelli lemma, is a partial
converse of the first Borel–Cantelli lemma. The lemma states that, under certain conditions, an event will have probability of either zero or one. Accordingly, it is the best-known of a class of similar theorems, known as zero-one laws. Other examples include
Kolmogorov's zero–one law and the
Hewitt–Savage zero–one law.
Statement of lemma for probability spaces
Let E1,E2,... be a sequence of events in some
probability space.
The Borel–Cantelli lemma states:[3][4]
Borel–Cantelli lemma — If the sum of the probabilities of the events {En} is finite
then the probability that infinitely many of them occur is 0, that is,
Here, "lim sup" denotes
limit supremum of the sequence of events, and each event is a set of outcomes. That is, lim sup En is the set of outcomes that occur infinitely many times within the infinite sequence of events (En). Explicitly,
The set lim sup En is sometimes denoted {En i.o. }, where "i.o." stands for "infinitely often". The theorem therefore asserts that if the sum of the probabilities of the events En is finite, then the set of all outcomes that are "repeated" infinitely many times must occur with probability zero. Note that no assumption of
independence is required.
Example
Suppose (Xn) is a sequence of
random variables with Pr(Xn = 0) = 1/n2 for each n. The probability that Xn = 0 occurs for infinitely many n is equivalent to the probability of the intersection of infinitely many [Xn = 0] events. The intersection of infinitely many such events is a set of outcomes common to all of them. However, the sum ΣPr(Xn = 0) converges to π2/6 ≈ 1.645 < ∞, and so the Borel–Cantelli Lemma states that the set of outcomes that are common to infinitely many such events occurs with probability zero. Hence, the probability of Xn = 0 occurring for infinitely many n is 0.
Almost surely (i.e., with probability 1), Xn is nonzero for all but finitely many n.
For general
measure spaces, the Borel–Cantelli lemma takes the following form:
Borel–Cantelli Lemma for measure spaces — Let μ be a (positive)
measure on a set X, with
σ-algebraF, and let (An) be a sequence in F. If
then
Converse result
A related result, sometimes called the second Borel–Cantelli lemma, is a partial converse of the first Borel–Cantelli lemma. The lemma states: If the events En are
independent and the sum of the probabilities of the En diverges to infinity, then the probability that infinitely many of them occur is 1. That is:[4]
Second Borel–Cantelli Lemma — If and the events are independent, then
The assumption of independence can be weakened to
pairwise independence, but in that case the proof is more difficult.
The lemma can be applied to give a covering theorem in Rn. Specifically (
Stein 1993, Lemma X.2.1), if Ej is a collection of
Lebesgue measurable subsets of a
compact set in Rn such that
then there is a sequence Fj of translates
such that
apart from a set of measure zero.
Proof
Suppose that and the events are independent. It is sufficient to show the event that the En's did not occur for infinitely many values of n has probability 0. This is just to say that it is sufficient to show that
Noting that:
it is enough to show: . Since the are independent:
Another related result is the so-called counterpart of the Borel–Cantelli lemma. It is a counterpart of the
Lemma in the sense that it gives a necessary and sufficient condition for the limsup to be 1 by replacing the independence assumption by the completely different assumption that is monotone increasing for sufficiently large indices. This Lemma says:
Let be such that ,
and let denote the complement of . Then the probability of infinitely many occur (that is, at least one occurs) is one if and only if there exists a strictly increasing sequence of positive integers such that
This simple result can be useful in problems such as for instance those involving hitting probabilities for
stochastic process with the choice of the sequence usually being the essence.
Kochen–Stone
Let be a sequence of events with and
Then there is a positive probability that occur infinitely often.