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Expectation-Maximization

An iterative algorithm for finding maximum likelihood estimates in models with latent variables.

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2 stops · basics first
  1. Maximum Likelihood Estimation ✓ understood

    Finding model parameters that maximize the probability of observing the training data.

  2. Latent Variable ✓ understood

    Hidden or unobserved variables in a model that influence observed data but aren't directly measured.

  3. Expectation-Maximization · you are here ✓ understood

An iterative algorithm for finding maximum likelihood estimates in models with latent variables.

This concept is essential for understanding machine learning fundamentals and forms a key part of modern AI systems.

  • Latent Variable
  • MLE
  • Clustering

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Maximum Likelihood Estimation Latent Variable
Expectation-Maximization

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Foundations Clustering An unsupervised learning technique that groups similar data points together based on their features or characteristics. Foundations Hidden Markov Model A statistical model with hidden states that transition probabilistically, generating observable outputs. Foundations Variational Inference Approximating complex distributions by optimizing over a simpler family, an alternative to MCMC. Foundations Gibbs Sampling An MCMC method that samples from conditional distributions to approximate joint distributions.