Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Concept

Random matrix

Related lectures (31)

Evolution of Density Matrices

Explores the evolution of density matrices in quantum optics, emphasizing super-operators and completely positive maps.

Evolution of Density Matrices

Covers the evolution of density matrices in quantum optics, focusing on quantum channels and amplitude damping.

Mach-Zehnder Interferometer

Explores the Mach-Zehnder interferometer, density matrices, and detection probabilities in quantum systems.

Market Response Functions

Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.

Untitled

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Random Matrix Theory: Replica Method

Covers random matrix theory and the replica method to compute quantities related to non-hermitic series using the Stieltjes transform.

Probability Review

Introduces subgaussian and subexponential random variables, conditional expectation, and Orlicz norms.

Shrinkage Estimation of Large Covariance Matrices

Explores shrinkage estimation of high-dimensional covariance matrices, comparing linear and nonlinear approaches for improved accuracy.

Quantum Chaos: Classical and Random Matrices

Explores classical and quantum chaos, magnetic monopoles, weak and strong coupling, and random matrix theory with a focus on Gaussian Unitary Ensemble.

Wigner's Semi-circular Law: The Sine Kernel

Covers Wigner's semi-circular law and the sine kernel in chaotic quantum systems.

Multi-arm Bandits: Regret and Exploration

Explores regret in multi-arm bandits, balancing exploration and exploitation for optimal decision-making in real-world applications.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Large Deviations Principle: Cramer's Theorem

Covers Cramer's theorem and Hoeffding's inequality in the context of the large deviations principle.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

Common Distributions: Moments and MGFs

Covers common distributions, moment generating functions, and covariance matrices in statistics for data science.

Mixture models: latent classes MOOC: Selected Topics on Discrete Choice

Explores latent classes in mixture models for discrete choice modeling using the Swissmetro dataset, discussing class-specific models and random parameters estimation.

Probability and Statistics

Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.

Related lectures (31)

Evolution of Density Matrices

Explores the evolution of density matrices in quantum optics, emphasizing super-operators and completely positive maps.

Evolution of Density Matrices

Covers the evolution of density matrices in quantum optics, focusing on quantum channels and amplitude damping.

Mach-Zehnder Interferometer

Explores the Mach-Zehnder interferometer, density matrices, and detection probabilities in quantum systems.

Market Response Functions

Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.

Untitled

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Random Matrix Theory: Replica Method

Covers random matrix theory and the replica method to compute quantities related to non-hermitic series using the Stieltjes transform.

Probability Review

Introduces subgaussian and subexponential random variables, conditional expectation, and Orlicz norms.

Shrinkage Estimation of Large Covariance Matrices

Explores shrinkage estimation of high-dimensional covariance matrices, comparing linear and nonlinear approaches for improved accuracy.

Quantum Chaos: Classical and Random Matrices

Explores classical and quantum chaos, magnetic monopoles, weak and strong coupling, and random matrix theory with a focus on Gaussian Unitary Ensemble.

Wigner's Semi-circular Law: The Sine Kernel

Covers Wigner's semi-circular law and the sine kernel in chaotic quantum systems.

Multi-arm Bandits: Regret and Exploration

Explores regret in multi-arm bandits, balancing exploration and exploitation for optimal decision-making in real-world applications.

Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Large Deviations Principle: Cramer's Theorem

Covers Cramer's theorem and Hoeffding's inequality in the context of the large deviations principle.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Mixture models: taste heterogeneity MOOC: Selected Topics on Discrete Choice

Explores mixture models in discrete choice and random parameters estimation results.

Common Distributions: Moments and MGFs

Covers common distributions, moment generating functions, and covariance matrices in statistics for data science.

Mixture models: latent classes MOOC: Selected Topics on Discrete Choice

Explores latent classes in mixture models for discrete choice modeling using the Swissmetro dataset, discussing class-specific models and random parameters estimation.

Probability and Statistics

Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.