Borel–Cantelli lemma

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 spacesEdit

Let E1,E2,... be a sequence of events in some probability space. The Borel–Cantelli lemma states:[3]

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.


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.


Let (En) be a sequence of events in some probability space.

The sequence of events   is non-increasing:


By continuity from above,


By subadditivity,


By original assumption,   As the series   converges,

as required.[4]

General measure spacesEdit

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-algebra F, and let (An) be a sequence in F. If


Converse resultEdit

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:

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 infinite monkey theorem, that endless typing at random will, with probability 1, eventually produce every finite text (such as the works of Shakespeare), amounts to the statement that a (not necessarily fair) coin tossed infinitely often will eventually come up Heads. This is a special case of the second Lemma.

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.


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:
The convergence test for infinite products guarantees that the product above is 0, if   diverges. This completes the proof.


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.


Let   be a sequence of events with   and   then there is a positive probability that   occur infinitely often.

See alsoEdit


  1. ^ E. Borel, "Les probabilités dénombrables et leurs applications arithmetiques" Rend. Circ. Mat. Palermo (2) 27 (1909) pp. 247–271.
  2. ^ F.P. Cantelli, "Sulla probabilità come limite della frequenza", Atti Accad. Naz. Lincei 26:1 (1917) pp.39–45.
  3. ^ Klenke, Achim (2006). Probability Theory. Springer-Verlag. ISBN 978-1-84800-047-6.
  4. ^ "Romik, Dan. Probability Theory Lecture Notes, Fall 2009, UC Davis" (PDF). Archived from the original (PDF) on 2010-06-14.
  • Prokhorov, A.V. (2001) [1994], "Borel–Cantelli lemma", Encyclopedia of Mathematics, EMS Press
  • Feller, William (1961), An Introduction to Probability Theory and Its Application, John Wiley & Sons.
  • Stein, Elias (1993), Harmonic analysis: Real-variable methods, orthogonality, and oscillatory integrals, Princeton University Press.
  • Bruss, F. Thomas (1980), "A counterpart of the Borel Cantelli Lemma", J. Appl. Probab., 17: 1094–1101.
  • Durrett, Rick. "Probability: Theory and Examples." Duxbury advanced series, Third Edition, Thomson Brooks/Cole, 2005.

External linksEdit