Measure Theory-Lebesgue Measurable

In summary, the Lebesgue measure of a set A is less than infinity, and so the function f(x) = \lambda(A\cap (-\infty,x)) is continous.
  • #1
WannaBe22
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Homework Statement


Let [tex]A \subseteq R [/tex] be a Lebesgue-Measurable set. Prove that if the Lebesgue measure of A is less than infinity , then the function [tex] f(x) = \lambda(A \cap (-\infty,x)) [/tex] is continous.

Homework Equations


The Attempt at a Solution


I'm really confused about the definition of [tex] \lambda (A) [/tex] where [tex] \lambda [/tex] is the Lebesgue-measure...I've tried taking an [tex] \epsilon >0 [/tex] and choosing some [tex] \delta >0 [/tex] for which if [tex] |x-x_0 | < \delta [/tex] then [tex]|f(x)-f(x_0)| <\epsilon [/tex] but I don't think this is the point...

I'll be delighted to get some guidance

Thanks !
 
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  • #2
Intuitively, [tex]f(x)[/tex] is the measure of the portion of [tex]A[/tex] "left of [tex]x[/tex]". So if [tex]x < x'[/tex], can you interpret [tex]f(x') - f(x)[/tex] in terms of measures?
 
  • #3
Intuitively, [tex] f(x')- f(x) [/tex] is measure of the portion of [tex] A [/tex] between [tex] x [/tex] and [tex] x' [/tex] ... Intuitively , this whole thing seems quite trivial...But when I try to get to the formal aspect of the soloution (as seen in "The attempt at a solution" part) , everything messes out... How can I make the intuition more formal ?
I really hope you'll be able to help me

Thanks !
 
  • #4
Here are two hints:

1. Given that intuitive description of [tex]f(x') - f(x)[/tex], try to come up with an upper bound for [tex]f(x') - f(x)[/tex] in terms of [tex]x' - x[/tex]. This is what you need to prove continuity. (What property of [tex]A[/tex] would give the largest possible value for [tex]f(x') - f(x)[/tex]?)

2. The fact that [tex]\lambda(A) < \infty[/tex] is irrelevant to the continuity of [tex]f[/tex]; you need it merely to define [tex]f[/tex].
 
  • #5
What is f(x)-f(x0)?

Hint: you must use that if [tex]A\subseteq B[/tex] and if those two sets have finite measure, then [tex]\lambda(B\setminus A)=\lambda(B)-\lambda(A)[/tex].
 
  • #6
Thanks a lot! your guidance was very helpful!
 

1. What is Measure Theory?

Measure Theory is a branch of mathematics that deals with the study of measures, which are mathematical tools used to assign a numerical value to sets. It provides a rigorous framework for understanding and analyzing the concept of size or volume of sets, and is particularly useful in the study of integration and probability theory.

2. What is Lebesgue Measurable?

A set is said to be Lebesgue measurable if it satisfies the Lebesgue measure, which is a generalized notion of length, area, or volume in higher dimensions. Intuitively, a Lebesgue measurable set has a well-defined size that can be calculated using a measure function. This concept is important in the development of Lebesgue integration, which extends the concept of Riemann integration to a broader class of functions.

3. What are the key properties of Lebesgue Measurable sets?

The key properties of Lebesgue measurable sets are that they are closed under countable unions and intersections, and that they form a sigma-algebra, which is a mathematical structure that allows for the definition of measures. Additionally, every open or closed set in Euclidean space is Lebesgue measurable, making it a more general and versatile concept than Riemann integrable sets.

4. What is the importance of Lebesgue Measurable sets in probability theory?

Lebesgue Measurable sets play a crucial role in probability theory as they allow for the definition of probability measures, which assign probabilities to events based on the size of the corresponding measurable set. This allows for a more rigorous and flexible approach to probability calculations, and is particularly useful in dealing with uncountable sets and distributions.

5. What are some applications of Measure Theory-Lebesgue Measurable?

Measure Theory and Lebesgue Measurable sets have a wide range of applications in various fields of mathematics, including analysis, topology, and probability theory. They are used to define and study a variety of mathematical objects, such as integrals, measures, and probability distributions, and provide a powerful framework for understanding and solving complex problems in these areas.

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