Covers the estimation of distributions using various methods such as minimum loss and expectation.
Explores implicit generative models, covering topics like method of moments, kernel choice, and robustness of estimators.
Explores diffusion models, focusing on generating samples from a distribution and the importance of denoising in the process.
Explores Maximum Likelihood Estimation, covering assumptions, properties, distribution, shrinkage estimation, and loss functions.
Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.
