Course

PHYS-512: Statistical physics of computation

Lectures in this course (83)

Explores replica symmetry and educated guessing in mathematical models.

Covers belief propagation on graphs, exploring computation challenges and heuristics, focusing on sparse random graphs' loop properties.

Covers the simplest model for overlapping events and their probabilities.

Explores the computation of mean field and effective field in message passing algorithms.

Explains entropy and free energy computation in graph coloring.

Covers the concept of symmetry in physics and its applications in various contexts.

Covers the Stochastic Block Model for community detection.

Explores the physics of food, focusing on equations and applications in food processing.

Covers the application of Belief Propagation in Stochastic Block Models, focusing on simplifying the process and solving it step by step.

Introduces signal processing basics, Fourier transform, and filtering techniques with practical applications and challenges.

Covers the computation of posterior distributions in Bayesian inference.

Explores belief propagation in random graphs and Bethe free entropy.

Explores magnetic phase transitions, ferromagnetism, Bethe lattice, Potts model, and free entropy.

Explores optimal detection in spinodal systems, discussing stability analysis and phase transitions.

Explores the relationship between matchings, entropy, and Bethe free entropy in random graphs.

Explores the transition from sparse to dense graphs and their real-world applications.

Covers the Spin Glass Game, belief propagation, and message passing algorithms.

Covers the fundamentals of Bayesian estimation, focusing on the application of Bayes' Theorem in scalar estimation.

Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.