Smoothing problem (stochastic processes): Difference between revisions

Revision as of 12:19, 23 June 2017

Revision as of 21:08, 30 June 2017

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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 [[Norbert Wiener]]

<ref name="wiener-report">1942, ''Extrapolation, Interpolation and Smoothing of Stationary Time Series''. A war-time classified report nicknamed "the yellow peril" because of the color of the cover and the difficulty of the subject. Published postwar 1949 [[MIT Press]]. http://www.isss.org/lumwiener.htm])</ref>

.<ref name="wiener-book">Wiener, Norbert (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. New York: Wiley. {{ISBN |0-262-73005-7}}.</ref>

A '''smoother''' is an algorithm or implementation that implements a solution to such problem. Please refer to the article [[Recursive Bayesian estimation]] for more information.

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Some variants include:<ref name="Sarkka-book">Simo Särkkä. Bayesian Filtering and Smoothing. Publisher: Cambridge University Press (5 Sept. 2013)

Language: English

{{ISBN |1107619289}}

{{ISBN |978-1107619289}}</ref>

* Rauch–Tung–Striebel (RTS) smoother