Markov Chains and Algorithm Applications

Related lectures (30)

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

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores the convergence of random walks on graphs and the properties of weighted adjacency matrices.

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

Covers the application of Markov chains and algorithms for function optimization and graph colorings.

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Covers the Leighton-Rao algorithm for finding the sparsest cut in a graph, focusing on its steps and theoretical foundations.

Explores the Szemerédi Regularity Lemma, e-regularity in bipartite graphs, supergraph structure, and induction techniques.

Explores maximizing diversity in document selection, graph clique determination, theorems on negative type, and convex optimization.

Explores interlacing families of polynomials and 1-sided Ramanujan graphs, focusing on their properties and construction methods.