Tutorial 3: Linear dynamical systems and the Kalman Filter
Tutorial 3: Linear dynamical systems and the Kalman Filter
This tutorial covers how to infer a latent model when our states are continuous. Particular attention is paid to the Kalman filter and it's mathematical foundation.
Overview of this tutorial:
- Review linear dynamical systems
- Learn about and implement the Kalman filter
- Explore how the Kalman filter can be used to smooth data from an eye-tracking experiment
Topics covered in this lesson
- Linear dynamical systems
- Kalman Filtering
- Fitting eye gaze data
- Review on Gaussian joint, marginal, and conditional distributions
- Kalman smoothing
- The Expectation-Maximization (EM) Algorithm
Prerequisites
Experience with Python Programming Language.
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