Notes
Welcome to my collection of notes on statistical physics, machine learning, and related topics.
Posts
Hopfield Networks as Models of Emergent Function in Biology — Mathematical descriptions of classic and modern Hopfield networks, storage capacity, projection methods, and energy-based models.
Gaussian Belief Propagation on Trees — Efficient inference on tree-structured graphical models using message passing, Gaussian convolutions, and the Schur complement.
Product Partition Models — Bayesian clustering and partition models for statistical analysis.
Minimax Entropy — Feature binding and selection using minimax entropy principles.
Matrix-Tree Theorem — Applications of the Matrix-Tree Theorem in probabilistic graphical models and chemical reaction networks.