Exploring Measure Theory: Sigma-Algebras, Premeasures, and Outer Measures

In summary, measure theory is a branch of mathematics that deals with the concepts of size, distance, and volume in a more abstract and rigorous way. It involves the use of sigma-algebras to define measures on sets, premeasures to obtain measures on larger sets, and outer measures to measure non-measurable sets. Measure theory has various real-world applications in fields such as physics, economics, and statistics.
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Definitions

If X is a set, an algebra A on X is a non-empty collection of subsets of X which is closed under complements with respect to X, and finite unions.

Given an algebra A, a premeasure on A is a function [itex]p\, :\, A \to [0,\, \infty][/itex] such that:
a) [itex]p(\emptyset ) = 0[/itex]
b) If B is a countable collection of disjoint elements of A whose union is in A, then [itex]p(\bigcup _{S \in B}S) = \sum _{S \in B}p(S)[/itex] (additivity)

A premeasure p on an algebra A of a set X is [itex]\mathbf{\sigma -finite}[/itex] iff there is a countable collection of elements of A, each with finite p-premeasure, whose union is X.

Given a set X, an outer measure w is a function from the power set of X to the extended non-negative reals which satisfies:
a) [itex]w(\emptyset ) = 0[/itex]
b) [itex]A \subset B \rightarrow w(A) \leq w(B)[/itex] (monotonicity)
c) [itex]w(\bigcup _{i=1}^{\infty} A_i) \leq \sum_{i=1}^{\infty}w(A_i)[/itex] (subadditivity)

A [itex]\mathbf{\sigma -algebra}[/itex] is an algebra which is closed under countable union.

A measure m on a [itex]\sigma -algebra[/itex] M is a function [itex]m\, :\, M \to [0\, ,\, \infty ][/itex] satisfying the same properties as a premeasure. Note, however, that since M is closed under countable union, the condition in property b) that the union be contained in the domain is redundant.

If f is a premeasure or a measure, and has domain D, then the induced outer measure, wf is defined by:

[tex]w_f(E) = \inf \left \{ \sum_{i=1} ^{\infty}f(A_i)\ :\ A_i \in D,\ E \subseteq \bigcup _{i=1} ^{\infty}A_i \right \}[/tex]

A measure is finite iff it doesn't map anything to infinity.

If E is a subset of a set X, and w is an outer measure on the power set of X, then E is w-measurable iff for every subset F of X:

[tex]w(F) = w(F-E) + w(F\cap E)[/tex]

Problems

1. Let M be a [itex]\sigma -algebra[/itex] on a set X, and let m be a measure with domain M. Let w be the induced outer measure by m. Suppose E is a subset of X satisfying w(E) = w(X) (E need not be in M). Prove that if A and B are in M, and [itex]A \cap E = B \cap E[/itex], then m(A) = m(B)

2. A is an algebra on X, [itex]A_{\sigma}[/itex] is the collection of all countable unions of sets in A, and [itex]A_{\sigma \delta}[/itex] is the collection of countable intersections of sets in [itex]A_{\sigma }[/itex]. Let p be a premeasure on A and w the induced outer measure. Prove that if w(E) is finite, then E is w-measurable iff there is a B in [itex]A_{\sigma \delta }[/itex] containing E such that w(B-E) = 0. Moreover, prove that if p is [itex]\sigma -finite[/itex], the restriction that w(E) be finite is superfluous.

For question 1, I've tried just playing around with taking outer measures of sets, applying the definition of measurability, using monotonicity and subadditivity, etc. but haven't gotten anywhere.

For question 2, I've actually proved most of it. It's just the last part, proving that if p is [itex]\sigma -finite[/itex] that the restriction that w(E) be finite is superfluous, which I can't get. The only time in proving the previous part where I used the restriction is when proving that if E is w-measurable, then there exists a B such that ... What happened was I find an appropriate B in [itex]A_{\sigma \delta }[/itex] containing E such that w(B) = w(E). After easily proving that B is w-measurable, I got:

[itex]w(B) = w(B-E) + w(B\cap E)[/itex]
w(E) = w(B-E) + w(E)

Finiteness of w(E) allowed me to cancel it from both sides, concluding w(B-E) = 0 as desired. But it seems that if I replace the assumption that w(E) is finite for the assumption that p is [itex]\sigma -finite[/itex], then I have to take a whole new approach to the proof. Any hints?

Thanks.
 
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Dear scientist,

For question 1, you can use the fact that for any sets A, B, C, D, we have: w(A) = w(B) if and only if w(A \cup C) = w(B \cup D) and w(A \cap C) = w(B \cap D). This can be easily proved using the properties of outer measures and the definition of measurability.

For question 2, you are on the right track. To prove that the restriction on w(E) being finite is superfluous, you can use the fact that if p is \sigma -finite, then for any set E, there exists a sequence of sets A_n in A such that E is contained in the union of A_n and p(A_n) is finite for all n. This can be used to show that for any w-measurable set E, there exists a B in A_{\sigma \delta } containing E such that w(B-E) = 0. You can then use this to prove the remaining part of the statement.
 

1. What is measure theory?

Measure theory is a branch of mathematics that deals with the concepts of size, distance, and volume in a more abstract and rigorous way. It provides a framework for understanding and quantifying the properties of sets, as well as the behavior of functions on those sets.

2. 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 an important concept in measure theory because it allows us to define a measure on a set of interest.

3. What is a premeasure?

A premeasure is a function that assigns a non-negative value to a set, which is a precursor to defining a measure. It is often used to obtain a measure on a larger set by restricting it to a smaller set.

4. What is an outer measure?

An outer measure is a function that assigns a non-negative value to a set, which is defined on the power set of a given set. It is used to measure the size of sets that may not be measurable with a traditional measure, such as infinite sets or irregularly shaped sets.

5. How is measure theory used in real-world applications?

Measure theory has many applications in various fields, such as physics, economics, and statistics. It is used to analyze and quantify the properties of objects and phenomena, such as the volume of a liquid, the length of a coastline, or the probability of an event occurring.

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