Hidden Semi-Markov Model: Implementation and Applications

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Resource Overview

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.

Detailed Documentation

In this document, we discuss Hidden Semi-Markov Models (HSMMs) and their application domains. HSMMs serve as powerful tools for probabilistic modeling and pattern recognition, extending standard Hidden Markov Models by explicitly modeling state duration distributions. These models can be applied to various problems including speech recognition, natural language processing, and remaining useful life prediction. Through parameter substitution in the model's state transition probabilities, duration distributions, and observation emissions, we can perform computations to obtain desired results. Key implementation aspects involve modifying the forward-backward algorithm to accommodate state durations and incorporating maximum likelihood estimation for parameter learning. Thus, Hidden Semi-Markov Models play a significant role in modern data analysis, particularly for sequential data with variable state persistence.

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