Lectures related to Factor graph

Covers Belief Propagation, a key method for both analysis and algorithm.

Explores hidden variables, graphical models, and computational gaps in neural network learning.

Discusses the unified formulation of the generalized linear model and the optimization of loss functions.

Explores the cavity method, Approximate Message Passing, and phase transitions in probabilistic models.