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.
This concept has the prerequisites:
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)
→ 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
- forward-backward algorithm
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 18.3.2-18.104.22.168, pages 643-644
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