Graphical Models: Probability Distributions and Factor Graphs

Related lectures (52)

Introduces the basics of graphs and networks, covering definitions, paths, trees, flows, circulation, and spanning trees.

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.

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

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

Explains message passing in graphical models and the matching problem in graph theory.

Explores Cheeger's inequalities for random walks on graphs and their implications.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Explores distances on graphs, cut norms, spanning trees, blockmodels, metrics, norms, and ERGMs in network data analysis.

Focuses on open problems and the importance of reformulating problems with better tools and intuition.

Introduces graph theory basics, Ramsey theory, and graph coloring concepts.

Explores belief propagation on graphs, emphasizing normalization, recursive relations, and iterative computation of the partition function.

Covers random variables, variances, and covariance, as well as the probability in random graphs.

Demonstrates the iterative process of applying Dijkstra's algorithm to find optimal paths.

Explores the Cartesian product operation on graphs and introduces the concept of cubes.

Explores a classic handshake problem at a party using graph representation.

Covers embedding graphs into trees with a focus on minimizing distortion and Bartal Tree embeddings.

Explores Bellman-Ford algorithm for negative weight graphs and currency exchange rates.

Covers the concept of graph sketching with a focus on connected components.

Covers probabilistic methods in combinatorics, monochromatic edges, 2-colorable graphs, and good 2-colorings.

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