03 — Probabilistic Models
Models that explicitly represent uncertainty through probability distributions, enabling principled inference over latent variables and calibrated predictions.
Sublayers
01 — Graphical Models
Bayesian networks, Markov random fields, d-separation, belief propagation, and HMMs.
02 — Latent Variable Models
Hidden Markov models, variational autoencoders, EM algorithm for latent structure.
03 — Bayesian Modeling
Probabilistic generative models, Naive Bayes, Gaussian mixture models, Bayesian inference.