The Lebesgue–Stieltjes integral is defined when is Borel-measurable and bounded and is of bounded variation in and right-continuous, or when is non-negative and is monotone and right-continuous. To start, assume that is non-negative and is monotone non-decreasing and right-continuous. Define and . By Carathéodory's extension theorem, there is a uniqueBorel measure on which agrees with on every interval. The measure arises from an outer measure given by the infimum taken over all coverings of by countably many semiopen intervals. This measure is sometimes called the Lebesgue–Stieltjes measure associated with. The Lebesgue–Stieltjes integral is defined as the Lebesgue integral of with respect to the measure in the usual way. If is non-increasing, then define the latter integral being defined by the preceding construction. If is of bounded variation and is bounded, then it is possible to write where is the total variation of in the interval, and. Both and are monotone non-decreasing. Now the Lebesgue–Stieltjes integral with respect to is defined by where the latter two integrals are well-defined by the preceding construction.
An alternative approach is to define the Lebesgue–Stieltjes integral as the Daniell integral that extends the usual Riemann–Stieltjes integral. Let be a non-decreasing right-continuous function on, and define to be the Riemann–Stieltjes integral for all continuous functions. The functional defines a Radon measure on. This functional can then be extended to the class of all non-negative functions by setting For Borel measurable functions, one has and either side of the identity then defines the Lebesgue–Stieltjes integral of. The outer measure is defined via where is the indicator function of. Integrators of bounded variation are handled as above by decomposing into positive and negative variations.
Example
Suppose that is a rectifiable curve in the plane and is Borel measurable. Then we may define the length of with respect to the Euclidean metric weighted by ρ to be where is the length of the restriction of to. This is sometimes called the -length of. This notion is quite useful for various applications: for example, in muddy terrain the speed in which a person can move may depend on how deep the mud is. If denotes the inverse of the walking speed at or near, then the -length of is the time it would take to traverse. The concept of extremal length uses this notion of the -length of curves and is useful in the study of conformal mappings.
Integration by parts
A function is said to be "regular" at a point if the right and left hand limits and exist, and the function takes at the average value Given two functions and of finite variation, if at each point either at least one of or is continuous or and are both regular, then an integration by parts formula for the Lebesgue–Stieltjes integral holds: Here the relevant Lebesgue–Stieltjes measures are associated with the right-continuous versions of the functions and ; that is, to and similarly The bounded inverval may be replaced with an unbounded interval, or provided that and are of finite variation on this unbounded interval. Complex-valued functions may be used as well. An alternative result, of significant importance in the theory ofstochastic calculus is the following. Given two functions and of finite variation, which are both right-continuous and have left-limits then where. This result can be seen as a precursor to Itô's lemma, and is of use in the general theory of stochastic integration. The final term is which arises from the quadratic covariation of and.
Related concepts
Lebesgue integration
When for all real, then is the Lebesgue measure, and the Lebesgue–Stieltjes integral of with respect to is equivalent to the Lebesgue integral of.
Where is a continuous real-valued function of a real variable and is a non-decreasing real function, the Lebesgue–Stieltjes integral is equivalent to the Riemann–Stieltjes integral, in which case we often write for the Lebesgue–Stieltjes integral, letting the measure remain implicit. This is particularly common in probability theory when is the cumulative distribution function of a real-valued random variable, in which case