Understanding Product Sigma Algebras: Definition, Notation, and Example

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SUMMARY

This discussion centers on understanding product sigma algebras as defined in "Real Analysis" by Folland. The product sigma-algebra is generated by the collection of inverse images of sets from individual sigma-algebras, specifically { πα-1(Eα) : Eα ∈ Mα, α ∈ A }. The participants clarify that the notation implies a universal quantification over all elements in the indexed collection. An example involving real numbers and sigma-algebras M1 and M2 illustrates the concept, confirming that πα-1(Eα) = Eα × R2.

PREREQUISITES
  • Understanding of sigma-algebras and their properties
  • Familiarity with Cartesian products of sets
  • Knowledge of the notation used in set theory and real analysis
  • Basic concepts of measure theory as related to sigma-algebras
NEXT STEPS
  • Study the concept of sigma-algebras in detail, focusing on their generation and properties
  • Learn about the implications of the product sigma-algebra in measure theory
  • Explore examples of product sigma-algebras in practical applications
  • Review Proposition 1.3 in Folland's "Real Analysis" for deeper insights into the topic
USEFUL FOR

Students of real analysis, mathematicians interested in measure theory, and anyone seeking to understand the complexities of product sigma algebras and their applications in mathematical contexts.

Aerostd
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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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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 definition:

[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]...
 
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.
 

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