- Lebesgue-Stieltjes integration
In measure-theoretic analysis and related branches of
mathematics , Lebesgue-Stieltjes integration generalizes Riemann-Stieltjes andLebesgue integration , preserving the many advantages of the latter in a more general measure-theoretic framework.Lebesgue-Stieltjes
integral s, named forHenri Leon Lebesgue andThomas Joannes Stieltjes , are also known as Lebesgue-Radon integrals or just Radon integrals, afterJohann Radon , to whom much of the theory of the present topic is due. They find common application in probability andstochastic process es, and in certain branches of analysis includingpotential theory .Definition
The Lebesgue-Stieltjes integral is defined when is Borel-measurableand boundedand is of
bounded variation in orwhen is non-negative and is monotone.To start, we assume that is non-negative and is monotone non-decreasing. In that case, for an interval ,define (this is just if , but we allow to be not necessarily closed).ByCarathéodory's extension theorem , there is a unique Borel measure on which agrees with on every interval .This measure is sometimes called [Halmos (1974), Sec. 15] the Lebesgue-Stieltjes measure associated with .Then is defined as the Lebesgue integral of with respect to the measure .If is non-increasing, then is defined as.If is of bounded variation and is bounded, then we may writewhere is the
total variation of in the interval , and .It can easily be shown that and are both monotone non-decreasing.Now the Lebesgue-Stieltjes integral is defined as, where the latter two integralsare well defined since and are non-decreasing.Example
Suppose that is a
rectifiable curve in the planeand is Borel measurable. Then we may define the lengthof with respect to the Euclidean metric weighted by tobe , where is the lengthof the restriction of to .This is sometimes called the -length of .This notion is quite useful forvarious applications: for example, in muddy terrain the speed in which a person can move maydepend on how deep the mud is. If denotes the inverse of the walking speedat or near , then the -length of is thetime it would take to traverse . The concept ofextremal length usesthis 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 the average value,:,at the limiting point. Given two functions and , if at each point either or is continuous, or if both and are regular, then there is an
integration by parts formula for the Lebesgue-Stieltjes integral::,where . Under a slight generalization of this formula, the extra conditions on and can be dropped. [cite journal |last=Hewitt |first=Edwin |year=1960 |month=5 |title=Integration by Parts for Stieltjes Integrals |journal=The American Mathematical Monthly |volume=67 |issue=5 |pages=419–423 |url=http://www.jstor.org/pss/2309287 |accessdate= 2008-04-23 |doi=10.2307/2309287 ]Related concepts
Lebesgue integration
When μ"v" is the
Lebesgue measure , then the Lebesgue-Stieltjes integral of "f" is equivalent to theLebesgue integral of "f".Riemann-Stieltjes integration and probability theory
Where "f" is a continuous real-valued function of a real variable and "v" 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 μ"v" remain implicit. This is particularly common inprobability theory when "v" is thecumulative distribution function of a real-valued random variable, in which case :(See the article on Riemann-Stieltjes integration for more detail on dealing with such cases.)Notes
References
*
* Shilov, G. E., and Gurevich, B. L., 1978. "Integral, Measure, and Derivative: A Unified Approach", Richard A. Silverman, trans. Dover Publications. ISBN 0-486-63519-8.External links
* Saks, Stanislaw (1937) " [http://matwbn.icm.edu.pl/kstresc.php?tom=7&wyd=10 Theory of the Integral.] "
* [http://www.probability.net/ www.probability.net Probability and foundations tutorial.]
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