Measurable function

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A function is Lebesgue measurable if and only if the preimage of each of the sets is a Lebesgue measurable set.

In mathematics, particularly in measure theory, a measurable function is a structure-preserving function between measurable spaces. For example, the notion of integrability can be defined for a real-valued measurable function on a measurable space. Specifically, a function between measurable spaces is said to be measurable if the preimage of each measurable set is measurable, analogous to the definition that a function between topological spaces is continuous if the preimage of each open set is open. A measurable function is said to be bimeasurable if it is bijective and its inverse is also measurable.[1]

For example, in probability theory, a measurable function on a probability space is known as a random variable. The σ-algebra of a probability space often represents the set of available information, and a function (in this context a random variable) is measurable if and only if it represents an outcome that is knowable based on the available information. In contrast, functions that are not Lebesgue measurable are generally considered pathological, at least in the field of analysis.

Formal definition[edit]

Let and be measurable spaces, meaning that and are sets equipped with respective -algebras and . A function is said to be measurable if the preimage of under is in for every ; i.e.

The notion of measurability depends on the sigma algebras and . To emphasize this dependency, if is a measurable function, we will write


This definition can be deceptively simple, however, as special care must be taken regarding the -algebras involved. In particular, when a function is said to be Lebesgue measurable, what is actually meant is that is a measurable function—that is, the domain and range represent different -algebras on the same underlying set. Here, is the -algebra of Lebesgue measurable sets, and is the Borel algebra on , the smallest -algebra containing all the open sets. As a result, the composition of Lebesgue-measurable functions need not be Lebesgue-measurable.

By convention a topological space is assumed to be equipped with the Borel algebra unless otherwise specified. Most commonly this space will be the real or complex numbers. For instance, a real-valued measurable function is a function for which the preimage of each Borel set is measurable. A complex-valued measurable function is defined analogously. In practice, some authors use measurable functions to refer only to real-valued measurable functions with respect to the Borel algebra.[2] If the values of the function lie in an infinite-dimensional vector space instead of or , usually other definitions of measurability are used, such as weak measurability and Bochner measurability.

Special measurable functions[edit]

  • If and are Borel spaces, a measurable function is also called a Borel function. Continuous functions are Borel functions but not all Borel functions are continuous. However, a measurable function is nearly a continuous function; see Luzin's theorem. If a Borel function happens to be a section of some map , it is called a Borel section.
  • A Lebesgue measurable function is a measurable function , where is the -algebra of Lebesgue measurable sets, and is the Borel algebra on the complex numbers . Lebesgue measurable functions are of interest in mathematical analysis because they can be integrated. In the case , is Lebesgue measurable iff is measurable for all . This is also equivalent to any of being measurable for all . Continuous functions, monotone functions, step functions, semicontinuous functions, Riemann-integrable functions, and functions of bounded variation are all Lebesgue measurable.[3] A function is measurable iff the real and imaginary parts are measurable.
  • Random variables are by definition measurable functions defined on sample spaces.

Properties of measurable functions[edit]

  • The sum and product of two complex-valued measurable functions are measurable.[4] So is the quotient, so long as there is no division by zero.[2]
  • The composition of measurable functions is measurable; i.e., if and are measurable functions, then so is .[2] But see the caveat regarding Lebesgue-measurable functions in the introduction.
  • The (pointwise) supremum, infimum, limit superior, and limit inferior of a sequence (viz., countably many) of real-valued measurable functions are all measurable as well.[2][5]
  • The pointwise limit of a sequence of measurable functions is measurable, where Y is a metric space (endowed with the Borel algebra). This is not true in general if Y is non-metrizable. Note that the corresponding statement for continuous functions requires stronger conditions than pointwise convergence, such as uniform convergence.[6][7]

Non-measurable functions[edit]

Real-valued functions encountered in applications tend to be measurable; however, it is not difficult to find non-measurable functions.

  • So long as there are non-measurable sets in a measure space, there are non-measurable functions from that space. If is some measurable space and is a non-measurable set, i.e. if , then the indicator function is non-measurable (where is equipped with the Borel algebra as usual), since the preimage of the measurable set is the non-measurable set . Here is given by
  • Any non-constant function can be made non-measurable by equipping the domain and range with appropriate -algebras. If is an arbitrary non-constant, real-valued function, then is non-measurable if is equipped with the trivial -algebra , since the preimage of any point in the range is some proper, nonempty subset of , and therefore does not lie in .

See also[edit]


  1. ^ Schervish, Mark J. (1997). Theory of statistics. New York, NY: Springer. p. 583. ISBN 978-0-387-94546-0. 
  2. ^ a b c d Strichartz, Robert (2000). The Way of Analysis. Jones and Bartlett. ISBN 0-7637-1497-6. 
  3. ^ Carothers, N. L. (2000). Real Analysis. Cambridge University Press. ISBN 0-521-49756-6. 
  4. ^ Folland, Gerald B. (1999). Real Analysis: Modern Techniques and their Applications. Wiley. ISBN 0-471-31716-0. 
  5. ^ Royden, H. L. (1988). Real Analysis. Prentice Hall. ISBN 0-02-404151-3. 
  6. ^ Dudley, R. M. (2002). Real Analysis and Probability (2 ed.). Cambridge University Press. ISBN 0-521-00754-2. 
  7. ^ Aliprantis, Charalambos D.; Border, Kim C. (2006). Infinite Dimensional Analysis, A Hitchhiker’s Guide (3 ed.). Springer. ISBN 978-3-540-29587-7. 

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