We now describe a simple problem that we will
analyze using the HMM tools.
Consider the HMM with the following parameters:
The output of the HMM is a time series with a
16-sample step size (i.e. the state is allowed to change
every 16 output samples). The output is Gaussian with
mean and variance depending on the state as follows:
For each 16-sample segment, the sample mean and standard deviation
are computed. This constitutes a 2-dimensional feature vector
that is the observation space of the HMM.