Conditional expectation, Lebesgue measure

In summary, the conversation discusses finding the conditional expectation E(X|Y) given a specific set of values for X and Y. The solution involves using the Lebesgue measure and integrating over the given interval. There is a question about which value to use for P(A_1), which is clarified by the fact that P is the Lebesgue measure.
  • #1
spitz
60
0

Homework Statement



Let Ω = [0,1] with the σ-field of Borel sets and let P be the Lebesgue measure on [0,1]. Find E(X|Y) if:

Homework Equations



[itex]X(w)=5w^2[/itex]

[itex]Y(w)= \left\{ \begin{array}{ll}
4 & \mbox{if $w \in [0,\frac{1}{4}]$} \\
2 & \mbox{if $w \in (\frac{1}{4},1]$} \\
\end{array}
\right.[/itex]

The Attempt at a Solution



For [itex]w\in A_1=[0,\frac{1}{4}][/itex]:

[itex]E(X|Y)(w)=E(X|A_1)=\frac{\int_{A_1}x\,dp}{P(A_1)}=\frac{1}{{P(A_1)}} \displaystyle\int_{0}^{1/4}5w^2\,dw[/itex]

Do I use [itex]P(A_1)=P(A_2)=\frac{1}{2}[/itex],

or [itex]P(A_1)=\frac{1}{4}[/itex], and [itex]P(A_2)=\frac{3}{4}[/itex] ?
 
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  • #2
spitz said:

Homework Statement



Let Ω = [0,1] with the σ-field of Borel sets and let P be the Lebesgue measure on [0,1]. Find E(X|Y) if:

Homework Equations



[itex]X(w)=5w^2[/itex]

[itex]Y(w)= \left\{ \begin{array}{ll}
4 & \mbox{if $w \in [0,\frac{1}{4}]$} \\
2 & \mbox{if $w \in (\frac{1}{4},1]$} \\
\end{array}
\right.[/itex]

The Attempt at a Solution



For [itex]w\in A_1=[0,\frac{1}{4}][/itex]:

[itex]E(X|Y)(w)=E(X|A_1)=\frac{\int_{A_1}x\,dp}{P(A_1)}=\frac{1}{{P(A_1)}} \displaystyle\int_{0}^{1/4}5w^2\,dw[/itex]

Do I use [itex]P(A_1)=P(A_2)=\frac{1}{2}[/itex],

or [itex]P(A_1)=\frac{1}{4}[/itex], and [itex]P(A_2)=\frac{3}{4}[/itex] ?

You said that P was Lebesgue measure, so what do you think is the Lebesgue measure of [0,1/4]?

RGV
 
  • #3
oh. 1/4
 

1. What is conditional expectation?

Conditional expectation is a mathematical concept that represents the expected value of a random variable given the knowledge of some other random variable. It is used to model the relationship between two random variables and can be calculated using the conditional probability formula.

2. How is conditional expectation related to probability?

Conditional expectation is closely related to probability because it involves calculating the expected value of a random variable, which is a fundamental concept in probability theory. It is used to predict the outcome of an event based on the knowledge of another event.

3. What is the Lebesgue measure?

The Lebesgue measure is a mathematical concept used to assign a numerical value to a set in a multi-dimensional space. It is a generalization of the one-dimensional concept of length and is used to measure the size or volume of a set in higher dimensions. It is named after French mathematician Henri Lebesgue.

4. How is the Lebesgue measure different from other measures?

The Lebesgue measure is different from other measures because it is defined for all subsets of a given space, not just for measurable sets. It also has the property of being translation-invariant, meaning that the measure of a set is the same regardless of its position in space. This makes it a more versatile and powerful tool for measuring and analyzing sets in higher dimensions.

5. What are the applications of conditional expectation and Lebesgue measure?

Conditional expectation and Lebesgue measure are used in various fields such as statistics, economics, and physics. In statistics, conditional expectation is used to model the relationship between variables in regression analysis. In economics, it is used in decision-making and forecasting models. In physics, it is used to calculate the expected value of physical quantities in quantum mechanics. Additionally, the Lebesgue measure has applications in geometric measure theory, functional analysis, and probability theory.

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