Lecture
Graphical Models: Representing Probabilistic Distributions
Related lectures (639)
Graphical Models: Joint Probability Distribution
Covers the concept of graphical models and joint probability distributions.
Statistical Analysis of Network Data
Introduces network data structures, models, and analysis techniques, emphasizing permutation invariance and Erdős-Rényi networks.
Frequency Domain Study: Acoustic Response Analysis
Explores the Frequency Domain study in COMSOL for analyzing acoustic responses to harmonic excitation in various fields.
Stochastic Block Model
Covers the Stochastic Block Model and its application in community detection, exploring its mathematical formulation and challenges.
Sparsest Cut: ARV Theorem
Covers the proof of the Bourgain's ARV Theorem, focusing on the finite set of points in a semi-metric space and the application of the ARV algorithm to find the sparsest cut in a graph.
Counterfactuals: SEM and D-Separation
Explores counterfactuals in SEMs and D-Separation in graphical models.
Bernoulli convolutions: The dimension of Bernoulli convolutions
Explores the dimension of Bernoulli convolutions, discussing fair tosses and polynomial roots.
Fixed Points in Graph Theory
Focuses on fixed points in graph theory and their implications in algorithms and analysis.
Graphical Models: Probability Distributions and Factor Graphs
Covers graphical models for probability distributions and factor graphs representation.
Air Pollution Analysis
Explores air pollution analysis using wind data, probability distributions, and trajectory models for air quality assessment.