高等概率论|Math 425/Math 340 / Stat 231/ST 8553/Math 551/MA 485/585Advanced Probability Theory代写

0

这是一份试验设计与方差分析作业代写的成功案

高等概率论|Math 425/Math 340 / Stat 231/ST 8553/Math 551/MA 485/585Advanced Probability Theory代写


$$
P\left[\lim {n \rightarrow \infty} X{n}=0\right]=1
$$
if
$$
\sum_{n} p_{n}<\infty
$$
To verify that observe that if
$$
\sum_{n} p_{n}=\sum_{n} P\left[X_{n}=1\right]<\infty,
$$
then by the Borel-Cantelli Lemma
$$
P\left(\left[X_{n}=1\right] \text { i.o. }\right)=0
$$
Taking complements, we find
$$
1=P\left(\limsup {n \rightarrow \infty}\left[X{n}=1\right]^{c}\right)=P\left(\liminf {n \rightarrow \infty}\left[X{n}=0\right]\right)=1 .
$$



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Math 425/Math 340 / Stat 231/ST 8553/Math 551/MA 485/585COURSE NOTES :

$$
P\left[X_{k}=1\right]=p_{k}=1-P\left[X_{k}=0\right]
$$
Then we assert that
$$
P\left[X_{n} \rightarrow 0\right]=1 \text { iff } \sum_{n} p_{k}<\infty
$$
To verify this assertion, we merely need to observe that
$$
P\left{\left[X_{n}=1\right] \text { i.o. }\right}=0
$$
iff
$$
\sum_{n} P\left[X_{n}=1\right]=\sum_{n} p_{n}<\infty
$$




高等概率论 | Advanced Probability Theory 代写 STAT 7060代考

0

这是一份umanitoba曼尼托巴大学STAT 7060作业代写的成功案

高等概率论 | Advanced Probability Theory 代写 STAT 7060代考
问题 1.

Let $\left{X_{n}, n \geq 1\right}$ be iid with common continuous distribution function $F(x)$. The continuity of $F$ implies
$$
P\left[X_{i}=X_{j}\right]=0
$$
so that if we define
$$
\text { [Ties }]=\bigcup_{t \neq \jmath}\left[X_{i}=X_{j}\right] \text {, }
$$


证明 .

then
$$
P[\text { Ties }]=0 .
$$
Call $X_{n}$ a record of the sequence if
$$
X_{n}>\bigvee_{t=1}^{n-1} X_{i}
$$

.

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STAT 7060 COURSE NOTES :


Also,
$$
\mathcal{C}{T{s}} \supset \mathcal{B}{\alpha}, \quad \forall \alpha \in T{s}
$$
(we can take $K={\alpha}$ ) and hence
$$
\sigma\left(\mathcal{C}{T{s}}\right) \supset \mathcal{B}{\alpha}, \quad \forall \alpha \in T{s}
$$
It follows that
$$
\sigma\left(\mathcal{C}{T{s}}\right) \supset \bigcup_{\alpha \in T_{s}} \mathcal{B}{\alpha} $$ and hence $$ \sigma\left(\mathcal{C}{T_{s}}\right) \supset \sigma\left(\bigcup_{\alpha \in T_{s}} \mathcal{B}{\alpha}\right)=: \bigvee{\alpha \in T_{s}} \mathcal{B}_{\alpha}
$$