(2.4 hours to learn)
The Kalman filter is an algorithm for inference in linear dynamical systems. Specifically, the task is to infer the posterior over the current latent state given past observations. It forms the basis for approximate inference algorithms in more general state space models.
This concept has the prerequisites:
Core resources (read/watch one of the following)
→ Artificial Intelligence: a Modern Approach
A textbook giving a broad overview of all of AI.
Location: Sections 15.2, up to "Smoothing" (pages 541-544) and 15.4 (pages 551-558)
Supplemental resources (the following are optional, but you may find them useful)
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 18.3.1-188.8.131.52, pages 640-642
→ Probabilistic Robotics
Location: Section 3.2.2-3.2.3, pages 43-45
→ 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
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