EM algorithm iteratively maximizes likelihood estimates with latent variables
Expectation–maximization algorithm
EM algorithm iteratively maximizes likelihood estimates with latent variables
The EM algorithm is designed to handle statistical models that include unobserved latent variables. It iteratively refines estimates of model parameters by alternating between expectation (E) and maximization (M) steps.
Example
In estimating a mixture of Gaussians, the EM algorithm alternates between estimating the parameters of each Gaussian component (M step) and updating the posterior distribution of the latent variables (E step).
Understanding the EM algorithm's iterative process is crucial for effectively estimating parameters in complex models with latent variables.
Related concepts
Fisher information
Fisher information measures information about unknown parameters
Minimum-variance unbiased estimator
MVUE achieves lower variance than any other unbiased estimator
Boosting (machine learning)
Boosting reduces bias in ML models
Metropolis–Hastings algorithm
Metropolis-Hastings algorithm samples from difficult distributions
Maximum a posteriori estimation
MAP estimation incorporates a prior P(θ)
Variational autoencoder
ELBO formula in variational inference
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