learning linear dynamical systems
We can perform maximum likelihood estimation for the parameters of a linear dynamical system using the EM algorithm. The E step involves running a Kalman smoother, and the M step involves maximum likelihood inference in multivariate Gaussians.
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 13.3.2, pages 642-644
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