If the function is a linear combination of states and inputs then the equations can be written in matrix notation like above.

This page shows the details for different matrix notations of a vector autoregression process with k variables.

An equivalent form using matrix notation is as follows:

The algorithm is written using matrix notation (1 based arrays instead of 0 based).

When implementing the algorithm, the part specified using matrix notation must be performed simultaneously.

We transform the probability distributions related to a given hidden Markov model into matrix notation as follows.

In matrix notation this model can be expressed as:

In matrix notation, we can write , where and .

The specifics of symbolic matrix notation varies widely, with some prevailing trends.

This can also be written in matrix notation as: