A weight function is a mathematical device used when performing a sum, integral, or average in order to give some elements more "weight" or influence on the result than other elements in the same set. They occur frequently in statistics and analysis, and are closely related to the concept of a measure. Weight functions can be employed in both discrete and continuous settings. They can be used to construct systems of calculus called "weighted calculus" and "meta-calculus".
In the discrete setting, a weight function is a positive function defined on a discrete set A, which is typically finite or countable. The weight function w(a): = 1 corresponds to the unweighted situation in which all elements have equal weight. One can then apply this weight to various concepts.
but given a weight function , the weighted sum is defined as
One common application of weighted sums arises in numerical integration.
In this case only the relative weights are relevant.
Weighted means are commonly used in statistics to compensate for the presence of bias. For a quantity f measured multiple independent times fi with variance , the best estimate of the signal is obtained by averaging all the measurements with weight , and the resulting variance is smaller than each of the independent measurements . The Maximum likelihood method weights the difference between fit and data using the same weights wi .
The terminology weight function arises from mechanics: if one has a collection of n objects on a lever, with weights (where weight is now interpreted in the physical sense) and locations :, then the lever will be in balance if the fulcrum of the lever is at the center of mass
which is also the weighted average of the positions .
In the continuous setting, a weight is a positive measure such as w(x)dx on some domain Ω,which is typically a subset of an Euclidean space , for instance Ω could be an interval [a,b]. Here dx is Lebesgue measure and is a non-negative measurable function. In this context, the weight function w(x) is sometimes referred to as a density.
can be generalized to the weighted integral
Note that one may need to require f to be absolutely integrable with respect to the weight w(x)dx in order for this integral to be finite.
If E is a subset of Ω, then the volume vol(E) of E can be generalized to the weighted volume
If Ω has finite non-zero weighted volume, then we can replace the unweighted average
by the weighted average
If and are two functions, one can generalize the unweighted inner product
to a weighted inner product
See the entry on Orthogonality for more details.
Published - July 2009
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