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What is Understanding the EM Algorithm Steps about?

The document discusses the EM algorithm, which is an iterative method used to find maximum likelihood estimates of parameters in probabilistic models with latent variables. It consists of expectation and maximization steps, where the expectation step estimates the latent variables given current parameters, and the maximization step updates the parameters given current estimates of the latent variables. The process repeats until convergence of the parameter values is reached.

Author

bhavana vashishtha

Language

EN