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The '''Smoothing problem''' (not to be confused with [[smoothing]] in signal processing and other contexts) refers to [[Recursive Bayesian estimation]] also known as [[Bayes filter]] is the problem of [[density estimation|estimating]] an unknown [[probability density function]] recursively over time using incremental incoming measurements. It is one of the main problems defined by [[Wiener]].
A '''smoother''' is an algorithm or implementation that implements a solution to such problem.
Not to be confused with blurring and smoothing using methods such as moving average. See [[smoothing]].
The [[Smoothing problem]] and [[Filtering problem]] are often considered a closely-related pair of problems.
[[Bayesian smoothing theory]]
===Example smoothers ===
Some variants include <ref name="Sarkka-book">Simo Sarkka. Bayesian Filtering and Smoothing.</ref>:
* Rauch–Tung–Striebel (RTS) smoother
* RTS smoother (ERTSS)
* Gauss–Hermite RTS smoother (GHRTSS)
* Cubature RTS smoother (CRTSS)
[[Category:Bayesian estimation]]
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