STA414
Machine Learning II70
Study paths
All entries
- Ancestral Sampling
- Backpropagation
- Bayesian Inference
- Belief Propagation (BP)
- Beta Distribution
- Conditional Distribution
- Conditional Independence
- Conditional Probability
- Continuous Random Variable
- Decision Theory
- Diagonal Gaussian Variational Approximation
- Discrete Random Variable
- ELBO
- Expectation-Maximization (EM)
- Expected Value
- Exponential Family
- Fisher Information
- Forward-Backward Algorithm
- Full-Covariance Gaussian
- Gamma Function
- Gaussian Mixture Model (GMM)
- Gibbs Sampling
- Hamiltonian Monte Carlo
- Hazard Function
- Hidden Markov Model (HMM)
- I-projection
- Importance Sampling
- Independence
- Indicator Function
- Joint Distribution
- Joint Posterior Density
- K-Means Algorithm
- KL Divergence
- Latent Variable Model
- Likelihood
- Marginal Distribution
- Marginal Likelihood / Evidence
- Maximum Likelihood Estimation (MLE)
- Mean-Field Limitation
- Mean-Field Variational Family
- Metropolis-Hastings Algorithm
- Monte Carlo Estimation
- Negative ELBO
- Normalization
- Posterior Correlation
- Posterior Distribution
- Prior Distribution
- Probability Density Function (PDF)
- Probability Distribution
- Probability Mass Function (PMF)
- Rejection Sampling
- Reparameterization Trick
- Stochastic Variational Inference
- Sufficient Statistics
- Survival Function
- Uniform Distribution
- Utility Function
- Valid Probability Density Function
- Variable Elimination
- Variational Family
- Variational Inference (VI)
- Variational Parameters
- Viterbi Algorithm
- Weibull Distribution
- STA414: Machine Learning II MOC
- Exam Pattern: EM on Gaussian Mixture
- Exam Pattern: VI and ELBO Derivation
- Graphic Demonstrations
- Example - Variational Inference & ELBO
- STA414 Notation Index