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Related lectures (9)

Information Theory: Basics

Covers the basics of information theory, entropy, and fixed points in graph colorings and the Ising model.

Consensus Algorithms: Weight Assignment and Applications

Explores the design of graph weights for consensus and applications in sensor networks.

Consensus in Networked Control Systems

Explores consensus in networked control systems through graph weight design and matrix properties.

Markov Chains and Algorithm Applications

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

Bipartite Graphs: Independent Sets

Explores bipartite graphs, independent sets, Shearer's Lemma, labeled graphs, and entropy analysis.

Statistical Analysis of Network Data: Noisy Sampled Networks

Explores statistical analysis of network data, covering noisy sampled networks, likelihood estimation, multilayer networks, and directed networks.

Integral Calculus: Techniques and Applications

Explores integral calculus techniques, areas under graphs, Darboux sums, and the fundamental theorem of calculus.

Labor Income Share

Analyzes a macroeconomic model focusing on labor income share and the effects of productivity shocks on GDP, consumption, investment, and stock prices.

Statistical Analysis of Network Data

Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.

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