Introduction
Abstract
Hidden Markov model (HMM) is a regime-shift model that assumes observation data are driven by hidden regimes (or states). The model has been used in many fields such as speech recognition, handwriting recognition, bio informatics, and financial economics. In this chapter, we will introduce a brief history of the HMM, its main concepts, formulas to calculate the likelihood of the observations, examples to demonstrate the formulas, and some types of the HMM.
Fields of Study & Subjects
Bayesian Methods and Mixture Models
Speech Recognition and Synthesis
Control Systems and Identification
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Pages
- 1–24
- Publication date
- 4 March 2026
- Language
- EN
- Document type
- Book chapter