Mean vector

In mathematics, mean has several different definitions depending on the context.

In probability and statistics, population mean and expected value are used synonymously to refer to one measure of the central tendency either of a probability distribution or of the random variable characterized by that distribution. In the case of a discrete probability distribution of a random variable X, the mean is equal to the sum over every possible value weighted by the probability of that value; that is, it is computed by taking the product of each possible value x of X and its probability P(x), and then adding all these products together, giving ${\displaystyle \mu =\sum xP(x)}$. An analogous formula applies to the case of a continuous probability distribution. Not every probability distribution has a defined mean; see the Cauchy distribution for an example. Moreover, for some distributions the mean is infinite: for example, when the probability of the value ${\displaystyle 2^{n}}$ is ${\displaystyle {\tfrac {1}{2^{n}}}}$ for n = 1, 2, 3, ....

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