Given a sequence of random variables $\{X_n \}_n$ I have obtained that: $$ X_n \overset{\mbox{a.s.}}{\longrightarrow} X \; \;\mbox{iff}\;\; \forall \epsilon>0 \; \lim_{n \to \infty} P(|X_n-X|< \epsilon)=1$$ Please, could anyboby check my proof and tell me where I did a mistake?
For $\epsilon>0$ and $n \in \mathbb{N}$ I defined: $$A_n^{\epsilon} \overset{def}{=} \{\omega: \; |X_n(\omega) - X(\omega)| < \epsilon\}$$ By resorting to the definition of $\underset{n \to \infty}{\text{lim inf } A_n^{\epsilon}}$ I got: $$P(\{\omega:\; \forall \epsilon>0\; \exists N\;\forall n\ge N |X_n(\omega) - X(\omega)| < \epsilon \}) = 1 \; \mbox{iff} \; \; \forall \epsilon > 0 \; P(\underset{n \to \infty}{\text{lim inf } A_n^{\epsilon}})=1$$ Being $\big\{ \bigcap_{n=1}^{\infty}A_n^\epsilon \big\}_n$ an increasing sequence, $\big\{ \bigcap_{n=i}^{m}A_n^\epsilon \big\}_m$ a decreasing sequence and knowing that $\bigcap_{n=i}^{\infty}A_n^\epsilon = \bigcap_{m=1}^{\infty}\bigcap_{n=i}^{m}A_n^\epsilon$, from continuity of measures from above and from below I obtained: $$P(\underset{n \to \infty}{\text{lim inf } A_n^{\epsilon}}) = P\big(\cup_{n=1}^{\infty}\cap_{i=n}^{\infty}A_{i}^{\epsilon} \big) = \lim_{n \to \infty}P(\cap{}_{i=n}^{\infty} A_i^\epsilon)=\lim_{n \to \infty}P(A_n^\epsilon)=1 $$