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For events $B_n\in {\mathcal{F}}_n$, put $p_n=\mathbb{P}(B_n\mid {\mathcal{F}}_{n-1})$. Then ${\sum }_n{1}_{B_n}<\infty$ almost surely on $\{{\sum }_n{p}_n<\infty \}$, while ${\sum }_n{1}_{B_n}=\infty$ almost surely on $\{{\sum }_n{p}_n=\infty \}$. The proof applies the martingale convergence theorem to compensated sums ${\sum }_{j\le n}(1_{B_j}-p_j)$ and uses Kronecker lemma in the divergent case.

For independent $N(0,1)$ increments, a random walk has $S_j\sim N(0,j)$. If $B$ is a bounded measurable set, its occupation time probability obeys $\mathbb{P}(S_j\in B)\le |B|/\sqrt{2\pi j}$. Consequently $\mathbb{E}{\sum }_j{1}_B(S_j)/j<\infty$, and Tonelli's theorem makes this series finite almost surely. Kronecker lemma then gives the displayed zero occupation density. This also shows why its empirical occupation probabilities cannot converge to a probability measure simultaneously on all bounded intervals and the whole real line: continuity from below would contradict the preserved total mass one.

The dominated convergence theorem states that if measurable functions converge almost everywhere and their absolute values are bounded by a single Lebesgue integrable function, their limit is integrable and their integrals converge to its integral.

Put $b_j=a_j/j$. The hypothesis makes $b_j$ summable. On the positive integers with counting measure define $f_n(j)=(j/n)b_j{1}_{j\le n}$. For each fixed $j$ it tends to zero and $0\le f_n(j)\le b_j$. Dominated convergence givesthe stated Kronecker lemma.

For a bounded interval union $B$, $S_j\sim N(0,j)$ has density bounded by $1/\sqrt{2\pi j}$. Hence $\mathbb{P}(S_j\in B)\le |B|/\sqrt{2\pi j}$, andBy Tonelli's theorem, the nonnegative series is finite almost surely. Kronecker lemma then makes the empirical occupation fraction of $B$ tend to zero. The family of finite rational-endpoint interval unions is countable, so intersecting their probability-one events proves simultaneous convergence for all of them. For the whole line, every indicator is one. ThusThere is no countably additive Borel measure extending these values: $\mathbb{R}={\bigcup }_{n\ge 1}(-n,n)$ would have measure zero by continuity from below, contrary to its assigned measure one. The escaped empirical mass cannot be captured by a probability measure on the real line.

This is the Gaussian random walk spends zero density of time in bounded sets phenomenon.

The strong law of large numbers states that for independent identically distributed integrable random variables,To prove it, set $Y_n=X_n{1}_{\{|X_n|\le n\}}$. The tail-sum formula gives ${\sum }_n\mathbb{P}(X_n\ne Y_n)<\infty$, so the Borel-Cantelli lemmas make the two sequences eventually equal. AlsoThe Kolmogorov convergence theorem implies that ${\sum }_n(Y_n-\mathbb{E}{Y}_n)/n$ converges almost surely, and Kronecker lemma yieldsFinally $\mathbb{E}{Y}_n\to \mu$, so the Cesàro mean of these expectations tends to $\mu$.

Set $p_n=\mathbb{P}(B_n\mid {\mathcal{F}}_{n-1})$, $S_n={\sum }_{j\le n}{1}_{B_j}$, and $A_n={\sum }_{j\le n}{p}_j$. Then $M_n=S_n-A_n$ is a martingale with bounded increments and conditional variance at most $p_n$. On $\{A_{\infty }<\infty \}$, localization and the $L^2$ martingale convergence theorem make $M_n$ converge, so the integer-valued increasing sequence $S_n$ is finite. On $\{A_{\infty }=\infty \}$, applying martingale convergence toand Kronecker lemma gives $M_n/A_n\to 0$. Hence $S_n/A_n\to 1$ and $S_n\to \infty$. This is the Conditional Borel-Cantelli lemma, and proves the two events equal almost surely.