# nLab empirical mean

Contents

### Context

#### Measure and probability theory

measure theory

probability theory

# Contents

## Idea

In probability theory, the empirical mean or empirical average is the average value of a quantity, with weights given by empirical frequencies.

For example, if we roll a die 3 times, and we obtain first $2$, then $3$, then $5$, the empirical mean is

$\frac{2+3+5}{3} \;=\; \frac{10}{3} \;\approx\; 3.33 .$

The name empirical mean denotes both the distribution obtained by sampling a finite amount of data, as well as the limit (when it exists) resulting from an infinite sequence of observations, usually generated from a stochastic process.

In statistics it is used as an estimator? of the expectation value of a random variable whenever it is possible to take iid samples.

## In measure-theoretic probability

Let $N$ be a finite set. We can view the product space $\mathbb{R}^N$ as the space of finite sequences $(x_1,\dots,x_n)$ of real numbers. The empirical mean of a finite sequence $(x_1,\dots,x_n)\in \mathbb{R}^N$ is the average

$\frac{x_1+\dots+x_n}{n} .$

Similarly, we can view the countable product $\mathbb{R}^\mathbb{N}$ as the space of infinite sequences $(x_1,x_2,x_3\dots)$ of real numbers. The empirical mean of a sequence $(x_1,x_2,x_3,\dots)\in \mathbb{R}^\mathbb{N}$ is the limit, if it exists,

$\lim_{n\to\infty} \frac{1}{n} \sum_{i=1}^n x_i .$

If the $x_i$ are random variables, and so they form a stochastic process (for example, if they are coin flips), the empirical distribution, if it exists, is a random variable as well.

(…)