Conditional expected value (using measure theory)

In summary, the property we want to show is that for any sub $\sigma$-algebra $\mathcal{G}$ of $\mathcal{F}$, the expected value of the conditional expected value of a random variable $X$ given $\mathcal{G}$ is equal to the expected value of $X$. This can be proven by setting $Z = \mathbb{E}[X|\mathcal{G}]$ and using the property that $Z$ is $\mathcal{G}$-measurable and $\forall G \in \mathcal{G}: \mathbb{E}[ZI_G] = \mathbb{E}[XI_G]$ for any random variable $X$.
  • #1
Barioth
49
0
Hi, I'm trying to show that
Givien a probability triplet \(\displaystyle (\theta,F,P)\)
with \(\displaystyle G\in F\) a sub sigma algebra
\(\displaystyle E(E(X|G))=E(X)\)

Now I want to use \(\displaystyle E(I_hE(X|G))=E(I_hX)\)
for every \(\displaystyle h\in G \)

since that's pretty much all I've for the definition of conditional expected value.

I know this property should use the definition of expected value, but I can't get it to work.

Thanks
 
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  • #2
Let $\mathcal{G} \subset \mathcal{F}$ be a sub $\sigma$-algebra of $\mathcal{F}$ then we have to prove:
$$\mathbb{E}[\mathbb{E}[X|\mathcal{G}]] = \mathbb{E}[X]$$

Set $Z = \mathbb{E}[X|\mathcal{G}]$ then by definition $Z$ is $\mathcal{G}$-measurable and $\forall G \in \mathcal{G}: \mathbb{E}[ZI_G] = \mathbb{E}[XI_G]$. Since $\mathcal{G}$ is a sub $\sigma$-algebra it has to contain $\Omega$ thus in particular $\mathbb{E}[ZI_{\Omega}] = \mathbb{E}[XI_{\Omega}]$ which means $\mathbb{E}[Z] = \mathbb{E}[X]$. Hence $\mathbb{E}[Z] = \mathbb{E}[\mathbb{E}[X|\mathcal{G}]] = \mathbb{E}[X]$.
 
  • #3
Thanks, very clean!
 

FAQ: Conditional expected value (using measure theory)

What is conditional expected value?

The conditional expected value is a statistical concept that measures the average outcome of a random variable given the knowledge of another random variable. It is calculated by taking the expected value of the conditional probability distribution.

How is conditional expected value different from regular expected value?

The regular expected value calculates the average outcome of a random variable without taking into consideration any other information. On the other hand, the conditional expected value takes into account the knowledge of another random variable, which can provide more accurate predictions.

What is the importance of using measure theory in calculating conditional expected value?

Measure theory is a branch of mathematics that deals with measuring sets and their properties. It provides a rigorous and systematic approach to calculating conditional expected value, ensuring that the results are accurate and reliable.

What are some real-life applications of conditional expected value?

Conditional expected value has various applications in the field of statistics, such as predicting stock prices, weather forecasting, and risk analysis. It is also widely used in fields like economics, finance, and engineering to make informed decisions based on probability and expected outcomes.

How can conditional expected value be used in decision-making?

Conditional expected value can be used to analyze the potential outcomes of different choices and make decisions based on the most favorable expected value. By considering all possible outcomes and their probabilities, decision-makers can make more informed and rational choices.

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