Hidden Semi-Markov Model: Implementation and Applications

The Hidden Semi-Markov Model enables computation through parameter substitution, suitable for pattern recognition, remaining useful life prediction, and other time-series analysis tasks. Implementation typically involves state duration modeling and forward-backward algorithm extensions.