# Kalman smoother

## Summary

Kalman smoothing is a posterior inference algorithm for linear dynamical systems (LDSs). It computes posterior marginals for all time steps conditioned on all of the observations. It is used in parameter learning for LDSs.

## Context

This concept has the prerequisites:

- Kalman filter (The forward pass of the Kalman smoother is the Kalman filter.)

## Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)

## Supplemental resources (the following are optional, but you may find them useful)

## -Paid-

→ Pattern Recognition and Machine Learning

A textbook for a graduate machine learning course, with a focus on Bayesian methods.

Location:
Section 13.3.1, pages 638-641

Additional dependencies:

- forward-backward algorithm

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 18.3.2-18.3.2.1, pages 643-644

## See also

-No Additional Notes-