Generative Learning Algorithms

Related lectures (31)

Page 3 of 4

Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.

Covers the likelihood ratio test for detection and estimation in statistical analysis.

Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.

Covers confidence intervals for Gaussian means, Student distribution, and Wald confidence intervals for maximum likelihood estimators.

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.