# Dirac measure

A diagram showing all possible subsets of a 3-point set {x,y,z}. The Dirac measure δx assigns a size of 1 to all sets in the upper-left half of the diagram and 0 to all sets in the lower-right half.

In mathematics, a Dirac measure assigns a size to a set based solely on whether it contains a fixed element x or not. It is one way of formalizing the idea of the Dirac delta function, an important tool in physics and other technical fields.

## Definition

A Dirac measure is a measure δx on a set X (with any σ-algebra of subsets of X) defined for a given xX and any (measurable) set AX by

${\displaystyle \delta _{x}(A)=1_{A}(x)={\begin{cases}0,&x\not \in A;\\1,&x\in A.\end{cases}}}$

where 1A is the indicator function of A.

The Dirac measure is a probability measure, and in terms of probability it represents the almost sure outcome x in the sample space X. We can also say that the measure is a single atom at x; however, treating the Dirac measure as an atomic measure is not correct when we consider the sequential definition of Dirac delta, as the limit of a delta sequence. The Dirac measures are the extreme points of the convex set of probability measures on X.

The name is a back-formation from the Dirac delta function, considered as a Schwartz distribution, for example on the real line; measures can be taken to be a special kind of distribution. The identity

${\displaystyle \int _{X}f(y)\,\mathrm {d} \delta _{x}(y)=f(x),}$

which, in the form

${\displaystyle \int _{X}f(y)\delta _{x}(y)\,\mathrm {d} y=f(x),}$

is often taken to be part of the definition of the "delta function", holds as a theorem of Lebesgue integration.

## Properties of the Dirac measure

Let δx denote the Dirac measure centred on some fixed point x in some measurable space (X, Σ).

• δx is a probability measure, and hence a finite measure.

Suppose that (X, T) is a topological space and that Σ is at least as fine as the Borel σ-algebra σ(T) on X.

• δx is a strictly positive measure if and only if the topology T is such that x lies within every non-empty open set, e.g. in the case of the trivial topology {∅, X}.
• Since δx is probability measure, it is also a locally finite measure.
• If X is a Hausdorff topological space with its Borel σ-algebra, then δx satisfies the condition to be an inner regular measure, since singleton sets such as {x} are always compact. Hence, δx is also a Radon measure.
• Assuming that the topology T is fine enough that {x} is closed, which is the case in most applications, the support of δx is {x}. (Otherwise, supp(δx) is the closure of {x} in (X, T).) Furthermore, δx is the only probability measure whose support is {x}.
• If X is n-dimensional Euclidean space n with its usual σ-algebra and n-dimensional Lebesgue measure λn, then δx is a singular measure with respect to λn: simply decompose n as A = n \ {x} and B = {x} and observe that δx(A) = λn(B) = 0.
• The Dirac measure is a sigma-finite measure

## Generalizations

A discrete measure is similar to the Dirac measure, except that it is concentrated at countably many points instead of a single point. More formally, a measure on the real line is called a discrete measure (in respect to the Lebesgue measure) if its support is at most a countable set.

## General references

• Dieudonné, Jean (1976). "Examples of measures". Treatise on analysis, Part 2. Academic Press. p. 100. ISBN 0-12-215502-5.
• Benedetto, John (1997). "§2.1.3 Definition, δ". Harmonic analysis and applications. CRC Press. p. 72. ISBN 0-8493-7879-6.