Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.
Covers the estimation of distributions using various methods such as minimum loss and expectation.
Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.
Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.
Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.
