# learning linear dynamical systems

## Summary

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.

## Context

This concept has the prerequisites:

- linear dynamical systems
- Expectation-Maximization algorithm (We use the EM algorithm to learn the parameters.)
- Kalman smoother (the E step requires computing marginals using a Kalman smoother.)
- maximum likelihood: multivariate Gaussians (The M step involves maximum likelihood estimation of multivariate Gaussians.)

## 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)

## -Paid-

→ 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

## See also

-No Additional Notes-