Understanding Product Sigma Algebras: Definition, Notation, and Example

In summary: E_{1} \times R_{1} .I think I am missing something here. Can someone explain this to me in a more linear fashion?In summary, the product sigma algebra is the sigma-algebra generated by \{ \prod_{\alpha \in A} E_{\alpha} : E_{\alpha} \in M_{\alpha} \} .
  • #1
Aerostd
18
0

Homework Statement



I should mention beforehand that I do not come from a math background so I may ask some trivial questions.

I am reading the book "Real Analysis" by Folland for a course I am taking and am attempting to understand a definition of product sigma algebra. It is stated in the book that if we have an indexed collection of non-empty sets, [tex] \{ X_{\alpha} \}_{\alpha \in A}[/tex], and we have

[tex] X = \prod_{x \in \alpha}^{} X_{\alpha} [/tex]

and

[tex] \pi_{\alpha} : X \to X_{\alpha} [/tex]

the coordinate maps. If [tex] M_{\alpha} [/tex] is a [tex] \sigma [/tex]-algebra on [tex] X_{\alpha} [/tex] for each [tex] \alpha [/tex], the product [tex] \sigma[/tex]-algebra on [tex] X [/tex] is the [tex] \sigma [/tex]-algebra generated by

{[tex] \pi_{\alpha}^{-1}(E_{\alpha}) : E_{\alpha} \in M_{\alpha}, \alpha \in A [/tex]}.Now the first time I read this definition(by first time I mean the thousandth time), I thought that one has to just pick one [tex] E_{\alpha} [/tex] from some [tex] M_{\alpha}[/tex] and take it's inverse map(pre image?) to get some collection of sets. Then generating a sigma algebra from that collection would yield the product sigma algebra. However, I was told that we have to take the inverse map of every [tex] E_{\alpha} [/tex] inside every [tex] M_{\alpha} [/tex]. I don't know how this is implied by the definition stated in the book but it makes more sense. Maybe I am not familiar with the notation because I would have expected some "for all" symbols in the definition somewhere.

Secondly, I wanted to think about what [tex] \pi_{\alpha}^{-1}(E_{\alpha}) [/tex] would look like just for a single [tex] E_{\alpha} [/tex].

The Attempt at a Solution

I tried to take an example like this, Consider:

[tex] \{ R_{1}, R_{2} \}[/tex], the real numbers. Then [tex] X = R_{1} \times R_{2} [/tex]. Suppose I have sigma algebras [tex] M_{1}[/tex] and [tex] M_{2} [/tex]. Now, What is [tex] \pi_{\alpha}^{-1} (E_{\alpha})[/tex] for some element in say [tex] M_{1} [/tex]?

Is it [tex] E_{\alpha} \times R_{2} [/tex]?

I don't know. Even this is just a guess. I'm hoping someone can give me some hints on understanding this since I am not able to easily find references on product sigma algebras.
 
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  • #2
Aerostd said:
Maybe I am not familiar with the notation because I would have expected some "for all" symbols in the definition somewhere.

Well, the "for all" symbol is implied by the notation. Let P be a certain property (I'll give examples later), then

[tex]\{x\in A~\vert~P(x)\}[/tex]

means that you take all elements in A which satisfy P. An example:

[tex]\{n^2~\vert~n\in \mathbb{N}\}[/tex]

By definition this means that you take all the squares in [tex]\mathbb{N}[/tex]. Thus, you take all the elements in [tex]\mathbb{N}[/tex], and you square it. Another example:

[tex]\{\{x\}~\vert~x\in \mathbb{R}\}[/tex]

means that you take all the [tex]x\in \mathbb{R}[/tex], and you take the singleton set of it.

So, your defintion:

[tex]\{\pi^{-1}(E_\alpha)~\vert~E_\alpha\in M_\alpha, \alpha\in A\}[/tex]

means that you take all the [tex]\alpha\in A[/tex] and all the [tex]E_\alpha\in M_\alpha[/tex], and you calculate [tex]\pi^{-1}(E_\alpha)[/tex].


Is it [tex] E_{\alpha} \times R_{2} [/tex]?

This is correct! [tex]\pi^{-1}(E_\alpha)=E_\alpha\times R_2[/tex]...
 
  • #3
Thanks a lot. I guess now I will move on to the next page in the book. I don't know if I should open a new thread or not but here goes.

This is Proposition 1.3 in Folland. I am interested more in the notation than in the proof given in the book. The proposition is

If [tex] A [/tex] is countable, then the product sigma-algebra is the sigma-algebra generated by [tex] \{ \prod_{\alpha \in A} E_{\alpha} : E_{\alpha} \in M_{\alpha} \} [/tex].

The proof given is this:

If [tex] E_{\alpha} \in M_{\alpha} [/tex], then [tex] \pi_{\alpha}^{-1} (E_{\alpha}) = \prod_{\beta \in A} E_{\beta} [/tex] where [tex] E_{\beta} = X_{\beta} [/tex] for [tex] \beta \notin \alpha [/tex].

On the other hand, [tex] \prod_{\alpha \in A} E_{\alpha} = \cap_{\alpha \in A} \pi_{\alpha}^{-1} (E_{\alpha}) [/tex]

Next he uses a lemma to prove this. I am more interested in the two equalities he uses.

Before anything I will ask the stupid question. Shouldn't one of the elements in [tex] M_{\alpha} [/tex] be the empty set? That is there should be some [tex] E_{j}[/tex] which will just be the empty set. Then the cartesian product of all elements in [tex] M_{\alpha}[/tex] should be the empty set. Where am I goofing up?

Anyway again I consider my example where I have [tex] \{ R_{1}, R_{2} \} [/tex], the reals and then have [tex] M_{1} [/tex] for [tex] R_{1} [/tex] and [tex] M_{2} [/tex] for [tex] R_{2} [/tex].

Now suppose I consider [tex] E_{1} [/tex] in [tex] M_{1} [/tex]. Then the first equality should be [tex] \pi_{1}^{-1} E_{1} = E_{1} \times R_{2} [/tex], which I can understand.

Next, in the second equality, the LHS I already asked about before. The RHS looks like it should be = [tex] E_{1} \times R_{1} \cap E_{2} \times R_{1} \cap E_{3} \times R_{1} \cap ... [/tex], but I don't see how this condenses to [tex] E_{1} \times E_{2} \times ...[/tex] which I am assuming is the RHS.
 

Related to Understanding Product Sigma Algebras: Definition, Notation, and Example

1. What is a product sigma algebra?

A product sigma algebra is a mathematical concept in measure theory that is used to describe the collection of all possible combinations of events or sets in a product space. It is denoted by the symbol Σ and is an important tool for understanding the probability of complex events.

2. How is a product sigma algebra different from a regular sigma algebra?

A product sigma algebra is different from a regular sigma algebra in that it operates in a multidimensional space, whereas a regular sigma algebra operates in a single dimension. This means that a product sigma algebra can account for the interactions between multiple events or variables, while a regular sigma algebra cannot.

3. What are some examples of product sigma algebras?

Some examples of product sigma algebras include the Borel sigma algebra, which is used to describe the probability of events in a real number space, and the Lebesgue sigma algebra, which is used to describe the probability of events in a continuous space. Other examples include the product of two or more discrete sigma algebras, which is used in combinatorial probability.

4. How is a product sigma algebra used in probability theory?

In probability theory, a product sigma algebra is used to define the probability of complex events by considering all possible combinations of simpler events. It allows for the calculation of joint probabilities, conditional probabilities, and other important concepts in probability theory.

5. What are some applications of product sigma algebras?

Product sigma algebras have many applications in mathematics and science. They are used in probability theory to model complex events and in statistics to analyze data. They are also used in economics, physics, and engineering to describe systems that involve multiple variables or events. Additionally, product sigma algebras are used in computer science to design algorithms and data structures for efficient data processing.

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