MATLAB Implementation of Hidden Markov Models (HMM)

We provide a comprehensive MATLAB implementation of Hidden Markov Models (HMM) that includes complete source files, demonstration scripts, and specialized programs for calculating HMM similarity metrics. The package contains algorithm implementations for key HMM operations including the forward-backward algorithm for probability calculation, Baum-Welch algorithm for parameter estimation, and Viterbi algorithm for optimal state sequence decoding. The similarity calculation module implements distance measures between HMM models using techniques like the Kullback-Leibler divergence approximation or symmetric similarity scores. Each component includes detailed documentation covering fundamental HMM concepts, practical applications, and implementation methodologies. The demonstration scripts showcase typical usage scenarios with sample datasets, allowing users to understand model training, sequence generation, and pattern recognition applications. Should you have any technical questions or require additional implementation guidance, please don't hesitate to contact us for professional support and assistance.