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Concept

Asymptotic theory (statistics)

Related lectures (32)

Quantum Field Theory II: Cross Section & Lifetime

Covers cross section, lifetime, quantum fluid, asymptotic states, discrete symmetries, and normal ordering in quantum field theory.

Quantum Fluids: Cross Section & Lifetime

Covers the concepts of cross section and lifetime in quantum fluids, focusing on transition and differential probabilities.

Asymptotic States and S-matrix

Covers the concept of asymptotic states and S-matrix in quantum field theory, focusing on the evolution of wave packets and the scattering states.

Decay Rate & Dyson Series

Explores cross section, decay rate, and Dyson series in turbulence, emphasizing proper split and Lorentz invariance.

Asymptotic States and S-matrix: Operators

Explores asymptotic states, S-matrix, and operators in quantum field theory, emphasizing the role of discrete symmetries and complete sets of states.

Asymptotic States and S-matrix

Explores asymptotic states, S-matrix, Lippmann-Schwinger equation, and energy robustness in quantum field theory.

Discrete Symmetries: Asymptotic States and S-matrix

Covers the concept of discrete symmetries, focusing on the introduction to asymptotic states and S-matrix.

Likelihood Ratio Test: Hypothesis Testing

Covers the Likelihood Ratio Test and hypothesis testing methods using Maximum Likelihood Estimators.

Generalized Linear Models: GLMs for Non-Gaussian Data

Explores Generalized Linear Models for non-Gaussian data, covering interpretation of natural link function, MLE asymptotic normality, deviance measures, residuals, and logistic regression.

Implicit Generative Models

Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.

Feynman Rules I: Asymptotic Statistic and Instantons

Covers the Feynman Rules, Asymptotic Statistics, Normal Ordering, and Instantons.

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Shrinkage Estimation of Large Covariance Matrices

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

Generalized Linear Models: Theory and Applications

Covers the theory and applications of Generalized Linear Models, including MLE, measures of fit, shrinkage, and special examples.

Gauss-Markov Theorem: Optimal Estimation

Explores the Gauss-Markov Theorem and the optimality of Least Squares Estimators in the Gaussian Linear model.

Learning Chemical Reaction Networks

Explores sparse learning of chemical reaction networks from trajectory data using data-based methods and learning approaches.

Quantum Field Theory: Feynman Rules

Covers the Feynman Rules in Quantum Field Theory, focusing on the proper split of terms and the solution of paradoxes.

Network Averaging: Theory and Applications

On network averaging covers labeled and unlabeled networks, statistical methods, challenges in network analysis, and practical considerations.

Maximum Likelihood Estimation Log in to Mediaspace to watch this video

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.

Likelihood Ratio Tests: Optimality and Extensions Log in to Mediaspace to watch this video

Covers Likelihood Ratio Tests, their optimality, and extensions in hypothesis testing, including Wilks' Theorem and the relationship with Confidence Intervals.