HMM_tutorial

Preface

This is a short book about Hidden Markov Models, their principles, and how they can be fitted and applied in practical cases.

It is organised in 4 chapters:

1. The introduction explains the principle of Hidden Markov Models through a simple probabilistic model involving boxes and balls.

2. The fitting part explains how a Hidden Markov Model is fit to an observed series of data to estimate its parameters.

3. The hidden states part explains how Hidden Markov Model are used to predict the hidden states behind a series of observations.

4. The case studies part shows how Hidden Markov Models can be used in two instances: one is the interpretation of geomorphic series along a river, the second is the interpretation of discursive content.