Concentration InequalitiesCovers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.
Node Degree and StrengthExplores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.
Distribution EstimationCovers the estimation of distributions using various methods such as minimum loss and expectation.
Data Issues in ResearchExplores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.
Handling Networks: Graph TheoryExplores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.
Handling Network DataCovers handling network data, types of graphs, centrality measures, and properties of real-world networks.
Property TestingCovers the concept of property testing using statistical methods.
Handling Network DataExplores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Statistical Analysis of Network DataExplores epidemics in network data, covering SIR model, basic reproductive ratio, percolation, directed networks, and maximum likelihood estimation.
Diffusion ModelsExplores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
