El Baum-Welch Algoritmo is a statistical algorithm used in the field of Inteligencia Artificial and Aprendizaje Automático to perform la estimación de parámetros for Modelos de Markov Ocultos (HMMs). It is a specific case of the Expectation-Maximization (EM) algorithm, which seeks to find the unknown parameters of a statistical model given incomplete data.
En muchas aplicaciones, como reconocimiento de voz, biological sequence analysis, and financial modeling, the underlying processes are not directly observable. HMMs provide a framework for modeling such systems, where the model consists of hidden states and observable outputs. The Baum-Welch Algorithm allows practitioners to improve their HMMs by refining the estimates of the model parameters (such as transition probabilities and emission probabilities) based on the observed sequences of data.
El algoritmo opera en dos pasos principales: el paso de Expectation (E-step) y el paso de Maximización (M-step).
- Paso E: In this step, the algorithm calculates the valor esperado of the log-likelihood function, given the current estimates of the model parameters.
- Paso M: Here, the algorithm updates the model parameters to maximize the expected log-likelihood found in the E-step.
The process of iterating between these two steps continues until convergence, meaning that the changes in the parameter estimates fall below a predefined threshold. The Baum-Welch Algorithm is particularly powerful because it can handle large datasets and complex models, making it a popular choice in various aplicaciones de IA.