Overview

Hidden Markov Models (HMMs) are a powerful statistical tool for modeling dynamic systems where the system's state is not directly observable. This particular implementation focuses on two essential algorithms within HMMs: the Viterbi algorithm and the Baum-Welch algorithm, offering a clear and concise Python 3.6+ solution powered by Numpy. For anyone keen on diving into the world of HMMs, this repository serves as an excellent springboard for both understanding and practical application.

The implementation is straightforward, making it accessible for users who may be new to the concepts behind HMMs. It provides not only code but also example scenarios, like a weather prediction model, to help contextualize the theoretical aspects of these algorithms. Such practical examples are crucial for grasping the utility of HMMs in real-world applications.

Features

Viterbi Algorithm: This algorithm is used for finding the most probable sequence of hidden states, given a sequence of observed events, enabling clear insight into the underlying process.
Baum-Welch Algorithm: As a special case of the Expectation-Maximization algorithm, this feature allows for estimating the parameters of the HMM, crucial for model adaptation and training.
Python 3.6+ Compatibility: Designed to work seamlessly with Python 3.6 and above, ensuring that users can integrate this into modern coding environments without compatibility issues.
Numpy Utilization: The use of Numpy enhances computational efficiency and simplicity, making mathematical operations straightforward and accessible.
Example Implementations: Provides simple examples, like a weather forecasting model, to illustrate the algorithms' practical applications, helping users understand theoretical concepts through real-world scenarios.
Clear Output Analysis: The implementation not only runs the algorithms but also provides clear interpretations of the results, aiding in understanding the output of each model.
Educational Resources: While the repository stands alone, it includes references to additional resources like tutorials and videos for those who want to delve deeper into HMM theory and application.

Jpowie01

HMM_Viterbi_BaumWelch