Optimizing Conditional Expectation

In summary, the conversation discusses the concept of maximizing the conditional expectation of a random variable X given a specific outcome s in its sample space S. It is suggested that this may be approached using functional-analytic techniques and may be seen as a variant of Factor Analysis/PCA. However, the definition of conditional expectation may vary depending on the definition of S.
  • #1
WWGD
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Hi all,
Let X be a random EDIT variable with (infinite) sample space S. Are there some results dealing with how to maximize

E(X|s ) (conditional expectation of X given s ) for s in S ?

Thanks.
 
Last edited:
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  • #2
If you know nothing about X or S, I don't see how a general strategy would work. The different expectation values can be completely independent.
If you know something about X or S, there can be nice ways to find the optimal s.
 
  • #3
Couldn't we see this as a sort of an infinite-dimensional variant of Factor Analysis/ PCA (where the elements of S are the infinite factors)? EDIT : Maybe we can use some functional-analytic techniques dealing with infinite-dimensional linear operators? I know there are generalizations to the infinite-dimensional case of , e.g., determinants, maybe there are generalizations of other aspects?
 
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  • #4
WWGD said:
Hi all,
Let X be a random EDIT variable with (infinite) sample space S. Are there some results dealing with how to maximize

E(X|s ) (conditional expectation of X given s ) for s in S ?

Thanks.
What is S supposed to be: the set of values which the random variable X can take, or the probability space on which it is defined?

In the latter case, X is a function from S to the real numbers R. When you condition on a particular outcome s in S, you fix the value of X; it's X(s). According to any sensible definition of conditional expectation, we should take E(X | s) to be X(s).

And now you want to find the maximal value which this can be? This is no longer a probability question.
 

Related to Optimizing Conditional Expectation

1. What is conditional expectation?

Conditional expectation is a statistical concept that measures the expected value of a random variable given the occurrence of certain conditions or events. It is denoted as E(X|Y) and represents the expected value of X given Y.

2. How is conditional expectation used in optimization?

Conditional expectation is often used as a tool in optimization problems to find the best decision or course of action given the available information. It can help in making more informed decisions by taking into account the likelihood of different outcomes.

3. What is the difference between conditional expectation and unconditional expectation?

Unconditional expectation, denoted as E(X), calculates the expected value of a random variable without any conditions or restrictions. On the other hand, conditional expectation considers the occurrence of certain events and calculates the expected value accordingly.

4. How is conditional expectation calculated?

Conditional expectation can be calculated using the formula E(X|Y) = ∑xP(X = x|Y) where x represents the different possible values of X and P(X = x|Y) represents the probability of X being equal to x given the occurrence of event Y.

5. How can optimizing conditional expectation improve decision making?

Optimizing conditional expectation can lead to more informed and optimal decision making by considering the likelihood of different outcomes and choosing the best course of action accordingly. It can also help in reducing risk and maximizing potential gains in various scenarios.

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