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We have to check convergence almost surely of $X_n$ to $0$

$$P(X_n=e^n)=\frac{1}{n+1}, \quad P(X_n=0)=1-\frac{1}{n+1}$$

I'm not sure I understand in practice the convergence Almost Surely and its difference from convergence in Probability.

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2 Answers 2

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For 2, here are two realizations which give different answers:

  1. Assume in addition that $(X_n)$ are independent. Then by the 2nd Borel-Cantelli's lemma together with the estimate $$ \sum_{n=0}^{\infty} \Bbb{P}(X_n = e^n) = \infty, $$ it follows that $\Bbb{P}(X_n = e^n \text{ i.o.}) = 1$.Therefore in this case, $(X_n)$ diverges almost surely.

  2. Now assume that $(X_n)$ is constructed in the following way: $(Z_n)_{n\geq1}$ are independent and $$ \Bbb{P}(Z_n = e) = \frac{n}{n+1}, \qquad \Bbb{P}(Z_n = 0) = \frac{1}{n+1}. $$ Now let $X_0 = 0$ and $X_n = Z_1 \cdots Z_n$. Then $$ \Bbb{P}(X_n = e^n) = \prod_{i=1}^{n} \Bbb{P}(Z_i = e) = \prod_{i=1}^{n}\frac{i}{i+1} = \frac{1}{n+1}, \qquad \Bbb{P}(X_n = 0) = 1 - \frac{1}{n+1}. $$ Notice that $\Bbb{P}(Z_n \neq 0 \text{ for all }n) = \prod_{n=1}^{\infty}\frac{n}{n+1} = 0$. Thus $Z_n = 0$ for some $n$ almost surely, which means that $X_n \to 0$ almost surely.

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  • $\begingroup$ But in the second example $P(X_n \neq 0 i.o.) = \sum_n P(X_n \neq 0) = \sum_k \prod_{j=1}^{k} P(Z_j \neq 0 ) = \frac{1}{2} + \frac{1}{2} \frac{2}{3} \ldots \frac{1}{n+1} \to \infty$ $\endgroup$
    – Alex
    Jul 5, 2020 at 21:49
  • $\begingroup$ @Alex, It is true that if $\sum_{n\geq 1}\mathbb{P}(X_n\neq 0)<\infty$ then $\mathbb{P}(X_n\neq0\text{ i.o.})=0$ by the Borel-Cantelli Lemma. However, since $X_n$'s are no more independent in the second example, the second Borel-Cantelli Lemma cannot be applied, and thus we cannot deduce anything from $\sum_{n\geq 1}\mathbb{P}(X_n\neq 0)=\infty$. $\endgroup$ Jul 5, 2020 at 21:53
  • $\begingroup$ Right, that's because $P(X_{n}=e^n \cap X_{n-1}=e^{n-1}) = P(X_n =e^n) =\frac{1}{n+1} \neq P(X_n=e^n)P(X_{n-1}=e^{n-1})$ $\endgroup$
    – Alex
    Jul 5, 2020 at 22:03
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This does not converges Almost Surely, though it converges in probability.

For as converge to hold (in rather informal terms) we need that "almost all realizations" of the sequence $\{X_n \}$ tend to zero. In our case, an individual sequence tends to zero only if there exists some $M$ such that $X_n=0$ for all $n\ge M$. Then, we need to compute this probability.

Let's call $A$ the set of such sequences, and for each $\{X_n \}\in A$ define $M$ as the greatest index such that $X_M\ne 0$. Lets call $A_M$ the sets grouped by this value, so $A= \cup_{M=0}^\infty A_M$

Then $$P(\{X_n\} \in A_M)= P(X_M\ne 0 \wedge X_{M+1}=0 \wedge X_{M+2}=0 \dots)=\frac{1}{M+1} \prod_{k=1}^ {\infty} \frac{M+k}{M+k+1} =0$$

This implies that $P(\{X_n\} \in A) =0$ . But for as convergence we wanted $P(\{X_n\} \in A) =1$

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    $\begingroup$ "This does not converges Almost Surely" True only if one assumes that $(X_n)$ is independent, which is not in the question. $\endgroup$
    – Did
    Nov 21, 2016 at 14:27

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