**Lesson 15 Elements of Discrete-time Gauss-Markov Random Sequences**

**Summary**

This is another transition lesson. Prior to studying recursive state estimation, we first review an important body of material on discrete-time Gauss-Markov random sequences. Most if not all of this material should be a review for a reader who has had courses in random processes and linear systems.

A *first-order Markov sequence* is one whose probability law depends only on the immediate past value of the random sequence; hence, the infinite past does not have to be remembered for such a sequence.

It is in this lesson that we provide a formal definition of *Gaussian white noise*. We also introduce the *basic state-variable model*. It consists of a state equation and a measurement ...