Sigma Algebras .... Axler, Page 26 ....

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In summary, the conversation discusses the implications of Sheldon Axler's definition of a ##\sigma##-algebra and its relation to the set of all subsets of ##\mathbb{R}##. It is mentioned that we cannot define Lebesgue measure on this ##\sigma##-algebra, but it is not stated that the set itself is not a ##\sigma##-algebra. The conversation also mentions the desire for the set to satisfy the properties in the definition of a ##\sigma##-algebra and thanks the contributors for their help.
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I need help in order to fully understand the implications of Axler's definition of a ##\sigma##-algebra ... ...
I am reading Sheldon Axler's book: Measure, Integration & Real Analysis ... and I am focused on Chapter 2: Measures ...

I need help in order to fully understand the implications of Axler's definition of a ##\sigma##-algebra ... ...

The relevant text reads as follows:

Axler - Sigma Algebres ... Page 26 .png


Now in the above text Axler implies that the set of all subsets of ##\mathbb{R}## is not a ##\sigma##-algebra ... ...

... BUT ... which of the three bullet points of the definition of a ##\sigma##-algebra is violated by the set of all subsets of ##\mathbb{R}## ... and how/why is it violated ...
Help will be much appreciated ...

Peter
 
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Math Amateur said:
Summary:: I need help in order to fully understand the implications of Axler's definition of a ##\sigma##-algebra ... ...

I am reading Sheldon Axler's book: Measure, Integration & Real Analysis ... and I am focused on Chapter 2: Measures ...

I need help in order to fully understand the implications of Axler's definition of a ##\sigma##-algebra ... ...

The relevant text reads as follows:

View attachment 267217

Now in the above text Axler implies that the set of all subsets of ##\mathbb{R}## is not a ##\sigma##-algebra ... ...

... BUT ... which of the three bullet points of the definition of a ##\sigma##-algebra is violated by the set of all subsets of ##\mathbb{R}## ... and how/why is it violated ...
Help will be much appreciated ...

Peter

Axler does not say that. The collection of all subsets is a ##\sigma##-algebra (trivially). Axler says that we cannot define Lebesgue-measure on this ##\sigma##-algebra and that's why we define Lebesgue measure on Borel sets.
 
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Math Amateur said:
Now in the above text Axler implies that the set of all subsets of ##\mathbb{R}## is not a ##\sigma##-algebra
No, he doesn't. The set of all subsets of ##\mathbb{R}## is obviously a ##\sigma##-algebra. It's just that we can't extend the notion of length on all the subsets of ##\mathbb{R}## without violating some highly desirable properties we want it to have (like countable additivity). So we force ourselves to give up on the idea of using all the subsets of ##\mathbb{R}## as the domain of our measure, but we still want this set of subsets to satisfy the properties in the definition of ##\sigma##-algebra.
 
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Thanks to Dragon27 and Math_QED for clarifying the issue ...

Much appreciate your help ...

Peter
 

1. What is a sigma algebra?

A sigma algebra is a collection of sets that satisfies certain properties, such as being closed under countable unions and complements. It is often used in measure theory to define a measure on a set.

2. Why are sigma algebras important in mathematics?

Sigma algebras are important because they provide a way to define a measure on a set, which is a fundamental concept in analysis and probability theory. They also allow for the rigorous definition of events and probabilities in a probability space.

3. How is a sigma algebra different from a regular algebra of sets?

A sigma algebra is different from a regular algebra of sets in that it is closed under countable unions, while a regular algebra is only closed under finite unions. This allows for a more general and flexible definition of a measure on a set.

4. What are some examples of sigma algebras?

Examples of sigma algebras include the Borel sigma algebra, which is the smallest sigma algebra containing all open sets in a topological space, and the Lebesgue sigma algebra, which is used to define the Lebesgue measure on the real line.

5. How are sigma algebras used in probability theory?

Sigma algebras are used in probability theory to define events and probabilities in a probability space. They allow for the calculation of probabilities for both discrete and continuous random variables, and are essential for understanding concepts such as independence and conditional probability.

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