  The detection of change-points in heterogeneous sequences is a statistical
challenge with many applications in fields such as finance, signal analysis and
biology. A wide variety of literature exists for finding an ideal set of
change-points for characterizing the data. In this tutorial we elaborate on the
Hidden Markov Model (HMM) and present two different frameworks for applying HMM
to change-point models. Then we provide a summary of two procedures for
inference in change-point analysis, which are particular cases of the
forward-backward algorithm for HMMs, and discuss common implementation
problems. Lastly, we provide two examples of the HMM methods on available data
sets and we shortly discuss about the applications to current genomics studies.
The R code used in the examples is provided in the appendix.
