EM, short for Expectation-Maximization, is an iterative algorithm used to find maximum likelihood estimates of parameters in statistical models, where the model depends on unobserved latent variables. It's particularly useful in scenarios involving incomplete data or mixture models.
Here's a breakdown of key aspects:
Purpose: To estimate parameters when some data is missing or hidden, or when the model involves a mixture of distributions. This is crucial in situations where direct optimization of the likelihood function is difficult or impossible.
The Two Steps: The EM algorithm consists of two alternating steps:
Iteration: The E and M steps are repeated iteratively until the parameter estimates converge, or a stopping criterion is met (e.g., a small change in the likelihood or parameters).
Applications: EM is widely used in various fields, including:
Advantages:
Disadvantages:
The EM algorithm provides a powerful tool for parameter estimation in the presence of incomplete data, making it a valuable technique in various statistical modeling applications.
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