MATLAB Implementation of Hidden Markov Models (HMM) with Algorithmic Explanations

This MATLAB-implemented Hidden Markov Model (HMM) provides an effective solution for analyzing and predicting complex data patterns. HMM is a probabilistic graphical model that addresses the challenge of predicting unobserved data by modeling relationships between hidden states and observable sequences. This model finds extensive applications in speech recognition, natural language processing, and bioinformatics. The implementation incorporates the Baum-Welch algorithm for parameter training, which iteratively adjusts transition and emission probabilities using forward-backward calculations. Key MATLAB functions include hmmtrain() for model estimation and hmmdecode() for state sequence prediction. The code structure typically involves initializing state transition matrices, defining observation probability distributions, and implementing the Viterbi algorithm for optimal path finding. MATLAB's matrix operations and statistical toolboxes significantly simplify the implementation of probability calculations and expectation-maximization procedures, enabling researchers to efficiently model sequential data while maintaining computational accuracy.