Hidden Semi-Markov Model Implementation with Code Documentation

This article provides a detailed explanation of the Hidden Semi-Markov Model (HSMM) implementation code, which consists of three core documents: initialization documentation, forward-backward algorithm implementation, and comprehensive parameter explanation documentation. Each component is thoroughly examined to ensure readers gain deep understanding of the code implementation.

The initialization document covers how to configure initial model parameters and prepare data for model training. It includes techniques for parameter initialization methods and data preprocessing strategies, with code examples demonstrating proper setup of state duration distributions and initial probability vectors.

The forward-backward algorithm document explains the implementation of this key algorithm for model training and inference. It breaks down each computational step including the forward probability calculation, backward probability computation, and the expectation-maximization procedure. Code snippets illustrate how to handle state duration probabilities and transition matrices efficiently.

Finally, the parameter explanation document provides detailed descriptions of each model parameter and their functional roles. It includes practical code examples and visualization diagrams showing how parameter adjustments affect model performance, particularly focusing on state transition probabilities, emission distributions, and duration parameters.

These documents collectively provide comprehensive understanding of HSMM implementation, enabling effective practical application of the model through clear algorithmic explanations and optimized code examples.