Information Theory: Basics

Related lectures (29)

Page 1 of 3

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Explores Belief Propagation for graph coloring and its convergence properties.

Covers the computation of Bethe free entropy and the interpretation of messages between variables and factors.

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

Explores the Cavity Method in Mean Field Theory, analyzing spins in an external field within a graph.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Explores Belief Propagation in graphical models, factor graphs, spin glass examples, Boltzmann distributions, and graph coloring properties.

Explores Markov chains, focusing on the coloring problem and algorithm analysis.

Explores graph traversal methods, spanning trees, and shortest paths using BFS and DFS.

Explores Prim's algorithm for minimum spanning trees and introduces the Traveling Salesman Problem.