Generalized Borel-Cantelli lemma #
This file proves Lévy's generalized Borel-Cantelli lemma which is a generalization of the Borel-Cantelli lemmas. With this generalization, one can easily deduce the Borel-Cantelli lemmas by choosing appropriate filtrations. This file also contains the one sided martingale bound which is required to prove the generalized Borel-Cantelli.
Note: the usual Borel-Cantelli lemmas are not in this file. See
MeasureTheory.measure_limsup_eq_zero
for the first (which does not depend on the results here),
and ProbabilityTheory.measure_limsup_eq_one
for the second (which does).
Main results #
MeasureTheory.Submartingale.bddAbove_iff_exists_tendsto
: the one sided martingale bound: given a submartingalef
with uniformly bounded differences, the set for whichf
converges is almost everywhere equal to the set for which it is bounded.MeasureTheory.ae_mem_limsup_atTop_iff
: Lévy's generalized Borel-Cantelli: given a filtrationℱ
and a sequence of setss
such thats n ∈ ℱ n
for alln
,limsup atTop s
is almost everywhere equal to the set for which∑ ℙ[s (n + 1)∣ℱ n] = ∞
.
One sided martingale bound #
leastGE f r n
is the stopping time corresponding to the first time f ≥ r
.
Equations
- MeasureTheory.leastGE f r n = MeasureTheory.hitting f (Set.Ici r) 0 n
Instances For
Alias of MeasureTheory.Submartingale.stoppedValue_leastGE_eLpNorm_le
.
Alias of MeasureTheory.Submartingale.stoppedValue_leastGE_eLpNorm_le'
.
This lemma is superseded by Submartingale.bddAbove_iff_exists_tendsto
.
One sided martingale bound: If f
is a submartingale which has uniformly bounded differences,
then for almost every ω
, f n ω
is bounded above (in n
) if and only if it converges.
Lévy's generalization of the Borel-Cantelli lemma #
Lévy's generalization of the Borel-Cantelli lemma states that: given a natural number indexed filtration $(\mathcal{F}_n)$, and a sequence of sets $(s_n)$ such that for all $n$, $s_n \in \mathcal{F}_n$, $limsup_n s_n$ is almost everywhere equal to the set for which $\sum_n \mathbb{P}[s_n \mid \mathcal{F}_n] = \infty$.
The proof strategy follows by constructing a martingale satisfying the one sided martingale bound. In particular, we define $$ f_n := \sum_{k < n} \mathbf{1}_{s_{n + 1}} - \mathbb{P}[s_{n + 1} \mid \mathcal{F}_n]. $$ Then, as a martingale is both a sub and a super-martingale, the set for which it is unbounded from above must agree with the set for which it is unbounded from below almost everywhere. Thus, it can only converge to $\pm \infty$ with probability 0. Thus, by considering $$ \limsup_n s_n = \{\sum_n \mathbf{1}_{s_n} = \infty\} $$ almost everywhere, the result follows.
Auxiliary definition required to prove Lévy's generalization of the Borel-Cantelli lemmas for which we will take the martingale part.
Equations
- MeasureTheory.BorelCantelli.process s n = ∑ k ∈ Finset.range n, (s (k + 1)).indicator 1
Instances For
An a.e. monotone adapted process f
with uniformly bounded differences converges to +∞
if
and only if its predictable part also converges to +∞
.
Lévy's generalization of the Borel-Cantelli lemma: given a sequence of sets s
and a
filtration ℱ
such that for all n
, s n
is ℱ n
-measurable, limsup s atTop
is almost
everywhere equal to the set for which ∑ k, ℙ(s (k + 1) | ℱ k) = ∞
.