# Expected Value Function

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An Expected Value Function is a mean function that returns a random variable's expected value.

**AKA:**E.**Context:****range:**Expected Value.- It can be created by an Expected Value Function Creation Task.
- …

**Example(s):**- Let X be a discrete random variable with pmf pX(x) and support S
_{X}. The expected value of X is given by E(X) = X x∈SX xpX(x). (Dubnicka, 2006c). - …

- Let X be a discrete random variable with pmf pX(x) and support S
**Counter-Example(s):****See:**First Moment, Moment Function, Sample Mean, Sample Variance, Point Estimate, Expected Utility.

## References

## References

### 2015

- http://en.wiktionary.org/wiki/Appendix:Glossary_of_probability_and_statistics#E
- expectation of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ("value"). … The concept is similar to the mean. The expected value of random variable
*X*is typically written E(X) or [math]\displaystyle{ \mu }[/math] (mu).

- expectation of a random variable is the sum of the probability of each possible outcome of the experiment multiplied by its payoff ("value"). … The concept is similar to the mean. The expected value of random variable

### 2006

- (Dubnicka, 2006c) ⇒ Suzanne R. Dubnicka. (2006). “Random Variables - STAT 510: Handout 3." Kansas State University, Introduction to Probability and Statistics I, STAT 510 - Fall 2006.
- QUOTE: The pmf of a discrete random variable and the pdf of a continuous random variable provides complete information about the probabilistic properties of a random variable. However, it is sometimes useful to employ summary measures. The most basic summary measure is the expectation or mean of a random variable X, denoted E(X), which can be thought of as an “average” value of a random variable.
- TERMINOLOGY : Let X be a discrete random variable with pmf pX(x) and support S
_{X}. The expected value of X is given by E(X) = X x∈SX xpX(x).

### 2003

- Liu, Yian-Kui, and Baoding Liu. "Expected value operator of random fuzzy variable and random fuzzy expected value models." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, no. 02 (2003): 195-215.
- QUOTE: … … In order to speed up the solution process, we will train a feedforward NN to approximate the expected value function U. We denote the network weights by a vector w. Hence the output of mapping implemented by the NN may be characterized by F(x,w). …