Hidden Markov Model Toolbox

The Hidden Markov Model (HMM) represents a widely-used statistical framework that operates on state transition principles. This model finds extensive applications across multiple domains including signal processing, natural language processing, and speech recognition. Within MATLAB 6.5, users can efficiently create and utilize a Hidden Markov Model toolbox to facilitate data analysis and modeling tasks. The toolbox incorporates various essential functions and utilities such as: - Construction of state transition probability matrices using matrix initialization methods - Generation of observation sequences through probability distribution sampling - Implementation of training algorithms including Baum-Welch (forward-backward) and Viterbi algorithms - Parameter estimation functions for model optimization - Probability calculation utilities for sequence evaluation These components enable systematic HMM implementation, supporting both supervised and unsupervised learning approaches for sequential data analysis.