Independence of sigma-algebras

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In summary, we have shown that the events A_n are independent if and only if the \sigma-algebras \sigma(A_n) are independent. This was demonstrated by considering two cases: first assuming independence of the \sigma-algebras and showing that this implies independence of the events, and then assuming independence of the events and showing that this implies independence of the \sigma-algebras. Both cases were proven using properties of independence and De Morgan's laws.
  • #1
Richard27182
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Homework Statement



Let [tex](A_n : n\in \mathbb{N})[/tex] be a sequence of events in a probability space. Show that the events [tex]A_n[/tex] are independent if and only if the [tex]\sigma[/tex]-algebras [tex]\sigma(A_n)=\{\emptyset, A_n, A_n^c, \Omega\}[/tex] are independent.

Homework Equations



For [tex]\sigma[/tex]-algebras [tex]\mathcal{A}_i \subseteq \mathcal{F}[/tex] to be independent, we need that if [tex]A_i \in \mathcal{A}_i\forall i[/tex], then the [tex]A_i[/tex] must be independent.

For events [tex]A_i:i\in I[/tex] to be independent, we need that , for any finite subset [tex]J\subseteq I[/tex]:
[tex]\mathbb{P}(\bigcap_{i\in J} A_i )=\prod_{i\in J} \mathbb{P}(A_i )[/tex]

The Attempt at a Solution



So first assume that the [tex]\sigma[/tex]-algebras are independent. Now [tex]A_i \in \sigma(A_i) \forall i[/tex], so the [tex]A_i[/tex] must be independent.

Now assume that the events [tex]A_i[/tex] are independent. Now take events [tex]B_i[/tex] such that [tex]B_i \in A_i \forall i[/tex] Now we need to check that for any finite subset [tex]J \subseteq \mathbb{N}[/tex], that we have
[tex]\mathbb{P}(\bigcap_{i\in J}B_i )=\prod_{i \in J} \mathbb{P}(B_i )[/tex]

Now if we have that for some [tex]i \in J,B_i =\emptyset[/tex], this is immediate (both sides are zero)
If we have that for some [tex]i\in J,B_i =\Omega[/tex], it reduces to the case where [tex]\nexists i \in J[/tex] s.t. [tex]B_i=\Omega[/tex]
So we are left with three cases to check:
1. [tex]B_i =A_i \forall i \in J[/tex]
2. [tex]B_i =A_i^c \forall i \in J[/tex]
3. [tex]B_i =A_i[/tex] for some [tex]i \in J[/tex] and [tex]B_i =A_i^c[/tex] for the rest of [tex]i\in J[/tex]
Case 1 follows immediately from the independence of the [tex]A_i[/tex]
Case 2 Labelling the sets in [tex]J[/tex] from [tex]1[/tex] to [tex]n[/tex]
[tex]
\mathbb{P}(\bigcap_{i=1}^{n}A_i^c)=\mathbb{P}((\bigcup_{i=1}^{n}A_i )^c)\text{(De Morgan's law)}[/tex]
[tex]=1-\mathbb{P}(\bigcup_{i=1}^{n} A_i)[/tex]
[tex]=1-\sum_{1}^{n}\mathbb{P}(A_i)+\sum_{i,j:1\le i<J\le n}\mathbb{P}(A_i \cap A_j)+...-(-1)^{n-1}\mathbb{P}(\bigcap_{i=1}^{n}A_i) \text{(by Inclusion-Exclusion)}[/tex]
[tex]=1-\sum_{i=1}^{n}\mathbb{P}(A_i)+\sum_{i,j:1\le i<J\le n}\mathbb{P}(A_i)\mathbb{P}( A_j)+...-(-1)^{n-1}\prod_{i-1}^{n}\mathbb{P}(A_i) \text{(by independence of} A_i )[/tex]
[tex]=\prod_{i=1}^{n}(1-\mathbb{P}(A_i))[/tex]
[tex]=\prod_{i=1}^{n}\mathbb{P}(A_i^c)
[/tex]
as required.
So all that remains is to check case 3. That is, we need to show that if (possibly relabelling the [tex]A_i[/tex]) [tex]B_i =A_i \text{ } i=1,...,r[/tex]
[tex]B_i=A_i^c\text{ } i=r+1,...,n[/tex] then
[tex]\mathbb{P}(\bigcap_{i=1}^{n}B_i}=\prod_{i=1}^{n}\mathbb{P}(B_i)[/tex]
[tex]\mathbb{P}(\bigcap_{i=1}^{n}B_{i})=\mathbb{P}((\bigcap_{i=1}^{r}B_i )\cap (\bigcap_{i=r+1}^{n}B_i ))
[/tex]

Unfortunately, I've got stuck here and am unable to make any further progress.
 
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  • #2
OK, so I have an answer:

[tex]\mathbb{P}((\bigcap_{i=1}^{r}B_i )\cap (\bigcap_{i=r+1}^{n}B_i ))[/tex]
[tex]=\mathbb{P}((\bigcap_{i=1}^{r}A_i )\cap (\bigcap_{i=r+1}^{n}A_i^c ))[/tex]
[tex]=\mathbb{P}((\bigcap_{i=1}^{r}A_i )\cap ((\bigcap_{i=r+1}^{n}A_i )^c))[/tex]
Now assuming that [tex]\mathbb{P}(\bigcap_{i=1}^{r}A_i )>0[/tex] (if not, the result follows immediately)
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )\mathbb{P}((\bigcap_{i=r+1}^{n}A_i )^c|(\bigcap_{i=i}^{r}A_i))[/tex]
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )(1-\mathbb{P}((\bigcap_{i=r+1}^{n}A_i )|(\bigcap_{i=i}^{r}A_i)))[/tex]
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )\mathbb{P}(\bigcap_{i=r+1}^{n}A_i^c )[/tex]
[tex]=\prod_{i=1}^{r}\mathbb{P}(A_i )\prod_{i=r+1}^{n}\mathbb{P}(A_i^c)[/tex]
[tex]=\prod_{i=1}^{n}\mathbb{P}(B_i)[/tex]

as required
 
  • #3
above answer does not work, I've applied De morgan's laws incorrectly
 
  • #4
OK, so I have another answer:

[tex]\mathbb{P}((\bigcap_{i=1}^{r}B_i )\cap (\bigcap_{i=r+1}^{n}B_i ))[/tex]
[tex]=\mathbb{P}((\bigcap_{i=1}^{r}A_i )\cap (\bigcap_{i=r+1}^{n}A_i^c ))[/tex]
[tex]=\mathbb{P}((\bigcap_{i=1}^{r}A_i )\cap ((\bigcup_{i=r+1}^{n}A_i )^c))[/tex]
Now assuming that [tex]\mathbb{P}(\bigcap_{i=1}^{r}A_i )>0[/tex] (if not, the result follows immediately)
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )\mathbb{P}((\bigcup_{i=r+1}^{n}A_i )^c|(\bigcap_{i=i}^{r}A_i))[/tex]
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )(1-\mathbb{P}((\bigcup_{i=r+1}^{n}A_i )|(\bigcap_{i=i}^{r}A_i)))[/tex]
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )(1-\mathbb{P}((\bigcup_{i=r+1}^{n}A_i )))[/tex]
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )\mathbb{P}((\bigcup_{i=r+1}^{n}A_i)^c)[/tex]
[tex]=\mathbb{P}(\bigcap_{i=1}^{r}A_i )\mathbb{P}(\bigcap_{i=r+1}^{n}A_i^c )[/tex]
[tex]=\prod_{i=1}^{r}\mathbb{P}(A_i )\prod_{i=r+1}^{n}\mathbb{P}(A_i^c)[/tex]
[tex]=\prod_{i=1}^{n}\mathbb{P}(B_i)[/tex]

as required
 

FAQ: Independence of sigma-algebras

What is the definition of independence of sigma-algebras?

Independence of sigma-algebras refers to a mathematical concept in the field of probability theory. It is a property that describes the relationship between two or more sigma-algebras, which are collections of sets that contain all possible outcomes of a random experiment. Two sigma-algebras are considered independent if the events in one sigma-algebra do not affect the probability of events in the other sigma-algebra.

Why is independence of sigma-algebras important?

Independence of sigma-algebras is important because it allows us to simplify complex probability calculations, making them more manageable and easier to understand. It also allows us to make certain assumptions and simplifications in statistical analyses, leading to more accurate and efficient results.

How is independence of sigma-algebras different from independence of events?

Independence of sigma-algebras and independence of events are related concepts but they are not the same. Independence of events refers to the concept that the occurrence of one event does not affect the probability of another event. Independence of sigma-algebras, on the other hand, refers to the relationship between collections of sets, rather than individual events.

Can sigma-algebras be independent if their underlying probability spaces are not independent?

Yes, it is possible for sigma-algebras to be independent even if their underlying probability spaces are not independent. This is because the independence of sigma-algebras is determined by the relationship between the collections of sets, rather than the underlying probability space.

How is independence of sigma-algebras used in real-world applications?

Independence of sigma-algebras is used in various fields such as statistics, economics, and engineering, to model and analyze complex systems with multiple random variables. It is also used in machine learning and artificial intelligence to build models that can make accurate predictions and decisions based on independent data. Additionally, it is used in finance and risk management to assess the probability of certain events and make informed decisions.

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