PSL_Book
  • 1. Introduction
  • 2. Linear Regression
  • 3. Variable Selection and Regularization
  • 4. Regression Trees and Ensemble
  • 5. Nonlinear Regression
  • 6. Clustering Analysis
  • 7. Latent Structure Models
    • 7.1. Model-based Clustering
    • 7.2. Mixture Models
    • 7.3. The EM Algorithm
    • 7.4. Latent Dirichlet Allocation Model
    • 7.5. Hidden Markov Models
  • 8. TBA
  • 9. Discriminant Analysis
  • 10. Logistic Regression
  • 11. Support Vector Machine
  • 12. Classification Trees and Boosting
  • 13. Recommender System
PSL_Book
  • 7. Latent Structure Models

7. Latent Structure Models

  • 7.1. Model-based Clustering
  • 7.2. Mixture Models
  • 7.3. The EM Algorithm
  • 7.4. Latent Dirichlet Allocation Model
  • 7.5. Hidden Markov Models
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