Indicator function

Indicator function
The graph of the indicator function of a two-dimensional subset of a square.

In mathematics, an indicator function or a characteristic function is a function defined on a set X that indicates membership of an element in a subset A of X, having the value 1 for all elements of A and the value 0 for all elements of X not in A.



The indicator function of a subset A of a set X is a function

\mathbf{1}_A : X \to \{ 0,1 \} \,

defined as

\mathbf{1}_A(x) =
1 &\text{if } x \in A, \\
0 &\text{if } x \notin A.

The Iverson bracket allows the equivalent notation, [x \in A], to be used instead of \mathbf{1}_A(x) .

The function \mathbf{1}_A is sometimes denoted \chi_A\! or \mathbf{I}_A\! or even just A\!. (The Greek letter χ appears because it is the initial letter of the Greek word characteristic.)

Remark on notation and terminology

A related concept in statistics is that of a dummy variable (this must not be confused with "dummy variables" as that term is usually used in mathematics, also called a bound variable).

The term "characteristic function" has an unrelated meaning in probability theory. For this reason, probabilists use the term indicator function for the function defined here almost exclusively, while mathematicians in other fields are more likely to use the term characteristic function to describe the function which indicates membership in a set.

Basic properties

The indicator or characteristic function of a subset A of some set X, maps elements of X to the range {0,1}.

This mapping is surjective only when A is a proper subset of X. If A \equiv X, then \mathbf{1}_{A} = 1. By a similar argument, if A \equiv \varnothing then \mathbf{1}_{A} = 0.

In the following, the dot represents multiplication, 1·1 = 1, 1·0 = 0 etc. "+" and "−" represent addition and subtraction. "\cap " and "\cup " is intersection and union, respectively.

If A and B are two subsets of X, then

\mathbf{1}_{A\cap B} = \min\{\mathbf{1}_A,\mathbf{1}_B\} = \mathbf{1}_A \cdot\mathbf{1}_B,\,
\mathbf{1}_{A\cup B} = \max\{{\mathbf{1}_A,\mathbf{1}_B}\} = \mathbf{1}_A + \mathbf{1}_B - \mathbf{1}_A \cdot\mathbf{1}_B,

and the indicator function of the complement of A i.e. AC is:

\mathbf{1}_{A^\complement} = 1-\mathbf{1}_A.

More generally, suppose A_1, \ldots, A_n is a collection of subsets of X. For any x \in X,

 \prod_{k \in I} ( 1 - \mathbf{1}_{A_k}(x))

is clearly a product of 0s and 1s. This product has the value 1 at precisely those x \in X which belong to none of the sets Ak and is 0 otherwise. That is

 \prod_{k \in I} ( 1 - \mathbf{1}_{A_k}) = \mathbf{1}_{X - \bigcup_{k} A_k} = 1 - \mathbf{1}_{\bigcup_{k} A_k}.

Expanding the product on the left hand side,

 \mathbf{1}_{\bigcup_{k} A_k}= 1 - \sum_{F \subseteq \{1, 2, \ldots, n\}} (-1)^{|F|} \mathbf{1}_{\bigcap_F A_k} = \sum_{\emptyset \neq F \subseteq \{1, 2, \ldots, n\}} (-1)^{|F|+1} \mathbf{1}_{\bigcap_F A_k}

where | F | is the cardinality of F. This is one form of the principle of inclusion-exclusion.

As suggested by the previous example, the indicator function is a useful notational device in combinatorics. The notation is used in other places as well, for instance in probability theory: if X is a probability space with probability measure \mathbb{P} and A is a measurable set, then \mathbf{1}_A becomes a random variable whose expected value is equal to the probability of A:

\operatorname{E}(\mathbf{1}_A)= \int_{X} \mathbf{1}_A(x)\,d\mathbb{P} = \int_{A} d\mathbb{P} = \operatorname{P}(A).\quad

This identity is used in a simple proof of Markov's inequality.

In many cases, such as order theory, the inverse of the indicator function may be defined. This is commonly called the generalized Möbius function, as a generalization of the inverse of the indicator function in elementary number theory, the Möbius function. (See paragraph below about the use of the inverse in classical recursion theory.)

Mean, variance and covariance

Given a probability space \textstyle (\Omega, \mathcal F, \mathbb P) with A \in \mathcal F, the indicator random variable \mathbf{1}_A : \Omega \rightarrow \Bbb{R} is defined by \mathbf{1}_A (\omega) = 1 if  \omega \in A, otherwise \mathbf{1}_A (\omega) = 0.

Mean: \operatorname{E}(\mathbf{1}_A (\omega)) = \operatorname{P}(A)
Variance: \operatorname{Var}(\mathbf{1}_A (\omega)) = \operatorname{P}(A)(1 - \operatorname{P}(A))
Covariance:  \operatorname{Cov}(\mathbf{1}_A (\omega), \mathbf{1}_B (\omega)) = \operatorname{P}(A \cap B) - \operatorname{P}(A)\operatorname{P}(B)

Characteristic function in recursion theory, Gödel's and Kleene's representing function

Kurt Gödel described the representing function in his 1934 paper "On Undecidable Propositions of Formal Mathematical Systems". (The paper appears on pp. 41-74 in Martin Davis ed. The Undecidable):

"There shall correspond to each class or relation R a representing function φ(x1, . . ., xn) = 0 if R(x1, . . ., xn) and φ(x1, . . ., xn)=1 if ~R(x1, . . ., xn)." (p. 42; the "~" indicates logical inversion i.e. "NOT")

Stephen Kleene (1952) (p. 227) offers up the same definition in the context of the primitive recursive functions as a function φ of a predicate P, takes on values 0 if the predicate is true and 1 if the predicate is false.

For example, because the product of characteristic functions φ12* . . . *φn = 0 whenever any one of the functions equals 0, it plays the role of logical OR: IF φ1=0 OR φ2=0 OR . . . OR φn=0 THEN their product is 0. What appears to the modern reader as the representing function's logical-inversion, i.e. the representing function is 0 when the function R is "true" or satisfied", plays a useful role in Kleene's definition of the logical functions OR, AND, and IMPLY (p. 228), the bounded- (p. 228) and unbounded- (p. 279ff) mu operators (Kleene (1952)) and the CASE function (p. 229).

Characteristic function in fuzzy set theory

In classical mathematics, characteristic functions of sets only take values 1 (members) or 0 (non-members). In fuzzy set theory, characteristic functions are generalized to take value in the real unit interval [0, 1], or more generally, in some algebra or structure (usually required to be at least a poset or lattice). Such generalized characteristic functions are more usually called membership functions, and the corresponding "sets" are called fuzzy sets. Fuzzy sets model the gradual change in the membership degree seen in many real-world predicates like "tall", "warm", etc.

See also


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