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Related lectures (30)
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Maximum Likelihood Theory & Applications
Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.
Maximum Likelihood Estimation: Theory
Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
Conditional Gaussian Generation
Explores the generation of multivariate Gaussian distributions and the challenges of factorizing covariance matrices.
Fluctuation-dissipation relations for reversible diffusions
Covers linear response, steady states, Girsanov transforms, and covariance limits in reversible diffusions.
Untitled
Unsupervised Learning: PCA & K-means
Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.
Fitting data with one Gauss function
Explains Gaussian functions, modeling data, likelihood function, and maximum likelihood optimization.
Central Limit Theorem: Properties and Applications
Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Extreme Value Models: Technical Derivation
Explores the technical derivation and properties of Multivariate Extreme Value models.
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