A Maximização da Expectativa (EM) Algoritmo is a powerful statistical technique used primarily for estimação de parâmetros in models that involve latent (hidden) variables. It is particularly useful in situations where the data is incomplete or has missing values, making direct estimação por máxima verossimilhança desafios.
O algoritmo EM consiste em duas etapas principais que são aplicadas de forma iterativa:
- Etapa de Expectativa (E-step): In this step, the algorithm computes the valor esperado of the log-likelihood function, considering the current estimate of the parameters and the latent variables. Essentially, it uses the known data to estimate the missing data based on the current model parameters.
- Etapa de Maximização (M-step): After the E-step, this step updates the model parameters by maximizing the expected log-likelihood found in the E-step. The new parameters are then used in the next iteration.
This iterative process continues until convergence, which typically means that the change in the estimated parameters falls below a pre-defined threshold. The EM algorithm is widely applicable in various fields, such as machine learning, computer vision, and bioinformatics, particularly for clustering tasks (e.g., Gaussian Mixture Models) and in training modelos de Markov ocultos.
Uma das principais vantagens do algoritmo EM é sua capacidade de lidar com incompletos effectively, making it a go-to choice for many researchers and practitioners dealing with real-world datasets where missing information is common.