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:
State |
Mean |
Var |

1 |
0 |
1 |

2 |
0 |
4 |

3 |
2 |
1 |

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.

Baggenstoss
2017-05-19