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== Book and Book Chapters == |
== Book and Book Chapters == |
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* Murphy, K. P. (2012). "Chapter 18: State space models". ''Machine Learning: A Probabilistic Perspective''. MIT Press. |
* Murphy, K. P. (2012). "Chapter 18: State space models". ''Machine Learning: A Probabilistic Perspective''. MIT Press. |
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* Koller, D., & Friedman, N. (2009). "Section 6.2.3.2: Linear Dynamical Systems". ''Probabilistic Graphical Models''. MIT Press. |
* Koller, D., & Friedman, N. (2009). "Section 6.2.3.2: Linear Dynamical Systems". ''Probabilistic Graphical Models''. MIT Press. |
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* Bishop, C. M. (2006). "Chapter 13: Sequential Data". ''Pattern Recognition and Machine Learning''. Springer. |
* Bishop, C. M. (2006). "Chapter 13: Sequential Data". ''Pattern Recognition and Machine Learning''. Springer. |
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* [[Linear Dynamical System|Linear Dynamical Systems / State Space Models]] |
* [[Linear Dynamical System|Linear Dynamical Systems / State Space Models]] |
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* Dynamic Bayesian Network |
* Dynamic Bayesian Network |
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| − | * [[Robotics|Robot Localization |
+ | * [[Robotics|Robot Localization]] |
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| + | [[Category:Control Theory]] |
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[[Category:Signal Processing]] |
[[Category:Signal Processing]] |
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[[Category:Probabilistic Graphical Models]] |
[[Category:Probabilistic Graphical Models]] |
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Latest revision as of 14:54, 24 October 2017
This page contains resources about Kalman filters and Linear Gaussian State Space Model.
Subfields and Concepts[]
- Bayesian Recursive Estimation / Bayes filter (generalization of the Kalman filter)
- Extended Kalman filter (EKF)
- Unscented Kalman filter (UKF)
- Iterated EKF
- Information filter
Book and Book Chapters[]
- Murphy, K. P. (2012). "Chapter 18: State space models". Machine Learning: A Probabilistic Perspective. MIT Press.
- Koller, D., & Friedman, N. (2009). "Section 6.2.3.2: Linear Dynamical Systems". Probabilistic Graphical Models. MIT Press.
- Bishop, C. M. (2006). "Chapter 13: Sequential Data". Pattern Recognition and Machine Learning. Springer.
- Grover, R., & Hwang, P. Y. (1996). Introduction to random signals and applied Kalman filtering. 3rd Ed. John Wiley & Sons.
Software[]
See also[]
- Estimation Theory
- Hidden Markov Model
- Markov Chain / Markov Process
- Linear Dynamical Systems / State Space Models
- Dynamic Bayesian Network
- Robot Localization