Hidden Markov Model Applications in Change-Point Analysis

Understand

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.