Editing Integral transform (section)

== Motivation ==

There are many classes of problems that are difficult to solve—or at least quite unwieldy algebraically—in their original representations. An integral transform "maps" an equation from its original "domain" into another domain, in which manipulating and solving the equation may be much easier than in the original domain. The solution can then be mapped back to the original domain with the inverse of the integral transform.

There are many applications of probability that rely on integral transforms, such as "pricing kernel" or [[stochastic discount factor]], or the smoothing of data recovered from robust statistics; see [[kernel (statistics)]].