In mathematics and in particular measure theory, a measurable function is a function between two measurable spaces such that the preimage of any measurable set is measurable, analogously to the definition that a function between topological spaces is continuous if the preimage of each open set is open. In real analysis, measurable functions are used in the definition of the Lebesgue integral. In probability theory, a measurable function on a probability space is known as a random variable.
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.
If is a measurable function, we will write
to emphasize the dependency on the -algebras and .
Term usage variations
The choice of -algebras in the definition above is sometimes implicit and left up to the context. For example, for , , or other topological space, the Borel algebra (containing all the open sets) is a common choice. Some authors define measurable functions as exclusively real-valued ones with respect to the Borel algebra.
Notable classes of measurable functions
- Random variables are by definition measurable functions defined on probability spaces.
- 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. A function is measurable iff the real and imaginary parts are measurable.
Properties of measurable functions
- The sum and product of two complex-valued measurable functions are measurable. So is the quotient, so long as there is no division by zero.
- If and are measurable functions, then so is their composition .
- If and are measurable functions, their composition need not be -measurable unless and are the same. Indeed, two Lebesgue-measurable functions may be constructed in such a way as to make their composition non-Lebesgue-measurable.
- 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.
- The pointwise limit of a sequence of measurable functions is measurable, where is a metric space (endowed with the Borel algebra). This is not true in general if is non-metrizable. Note that the corresponding statement for continuous functions requires stronger conditions than pointwise convergence, such as uniform convergence.
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 .
- Strichartz, Robert (2000). The Way of Analysis. Jones and Bartlett. ISBN 0-7637-1497-6.
- Carothers, N. L. (2000). Real Analysis. Cambridge University Press. ISBN 0-521-49756-6.
- Folland, Gerald B. (1999). Real Analysis: Modern Techniques and their Applications. Wiley. ISBN 0-471-31716-0.
- Royden, H. L. (1988). Real Analysis. Prentice Hall. ISBN 0-02-404151-3.
- Dudley, R. M. (2002). Real Analysis and Probability (2 ed.). Cambridge University Press. ISBN 0-521-00754-2.
- Aliprantis, Charalambos D.; Border, Kim C. (2006). Infinite Dimensional Analysis, A Hitchhiker’s Guide (3 ed.). Springer. ISBN 978-3-540-29587-7.