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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. Please refer to the article [[Recursive Bayesian estimation]] for more information.
The [[Smoothing problem]] and [[Filtering problem]] are often considered a closely-related pair of problems. They are studied in Bayesian smoothing theory.▼
Note: Not to be confused with blurring and smoothing using methods such as moving average. See [[smoothing]].▼
▲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.
===Example smoothers ===
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