Cuestionario vinculado a una guía · Medio Nivel

Prueba de algoritmo de maximización de expectativas

Siga el paso E y el paso M en modelos de variables latentes y distinga la mejora de la probabilidad de la optimización global o la validez del modelo.

Rutas de guía relacionadasAlgoritmo de maximización de expectativas
Pregunta 1 de 8

What does the E-step calculate using current parameters?

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Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

  1. What does the E-step calculate using current parameters?
    • Conditional expectations of complete-data quantities or log likelihood
    • The global maximum of observed likelihood
    • Hard labels for every hidden variable by necessity
    • A new validation dataset
  2. In a Gaussian mixture, what do responsibilities represent?
    • Fixed labels known before fitting
    • Posterior membership probabilities for observations across components
    • Component means multiplied by covariances
    • The number of EM iterations remaining
  3. What does the M-step do after expectations are computed?
    • Changes the number of features automatically
    • Replaces all observations with component labels
    • Maximizes the expected complete-data objective over parameters
    • Computes the validation likelihood only
  4. What guarantee does exact EM provide under suitable assumptions?
    • The selected model is scientifically correct
    • It finds the unique global optimum
    • Every hidden assignment becomes certain
    • Observed-data likelihood does not decrease at each iteration
  5. Different EM initializations end at different likelihoods. What does this suggest?
    • The E-step is unnecessary
    • The optimization is sensitive to starts and may have local optima
    • The data contain no latent variables
    • All parameter estimates are unbiased
  6. Why are mixture responsibilities not automatically hard assignments?
    • They are class labels copied from the target
    • They are always equal for every observation
    • They are probabilities that can distribute one observation across components
    • They represent only missing data
  7. What does a small likelihood improvement at stopping indicate?
    • The latent structure has causal meaning
    • The model is proven adequate
    • The global optimum is certain
    • The chosen numerical convergence criterion was met
  8. How does generalized EM differ from exact EM?
    • Its M-step may increase the expected objective without fully maximizing it
    • It removes the latent variables
    • It guarantees a global optimum
    • It replaces likelihood with classification accuracy