O Baum-Welch Algoritmo is a statistical algorithm used in the field of Inteligência Artificial and Aprendizado de Máquina to perform estimação de parâmetros 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.
Em muitas aplicações, como reconhecimento de fala, 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.
O algoritmo opera em duas etapas principais: a etapa de Expectation (E-step) e a etapa de Maximização (M-step).
- Etapa E: In this step, the algorithm calculates the valor esperado of the log-likelihood function, given the current estimates of the model parameters.
- Etapa 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 aplicações de IA.