Infinite Hidden Markov Model (IHMM) Sampling and Inference Algorithms

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Resource Overview

Implementation of Infinite Hidden Markov Model (IHMM) sampling and inference algorithms for pattern recognition and video anomaly detection, developed on MATLAB 2009 platform featuring state transition sampling, observation emission modeling, and forward-backward inference procedures

Detailed Documentation

This program implements sampling and inference algorithms for Infinite Hidden Markov Models (IHMM), designed for applications including pattern recognition and video anomaly detection. The implementation incorporates key Bayesian nonparametric methods such as the Chinese Restaurant Process for state transition modeling and Dirichlet Process mixtures for observation emissions. Developed on the MATLAB 2009 platform, the code features efficient forward-backward inference algorithms, Markov chain Monte Carlo sampling techniques, and handles infinite state spaces through truncation approximations. The implementation includes functions for parameter estimation, state sequence decoding, and likelihood computation suitable for large-scale temporal data analysis.

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