Smoothing problem (stochastic processes)

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 estimating an unknown probability density function recursively over time using incremental incoming measurements. It is one of the main problems defined by Norbert 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.

Example smoothers

Some variants include :

• Rauch–Tung–Striebel (RTS) smoother
• RTS smoother (ERTSS)
• Gauss–Hermite RTS smoother (GHRTSS)
• Cubature RTS smoother (CRTSS)

See Also

• Smoothing problem is closely related to the Filtering problem.
• Filtering (disambiguation)
• Filtering problem
• Not to be confused with Filter (signal processing)
• Kalman filter most famous filtering algorithm in the sense of 'filtering problem' and 'smoothing problem'.
• Smoothing (not to be confused with the Smoothing problem)
• Smoothing (disambiguation)
• Smoothing problem