Lectures related to Foundations of statistics

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

On hypothesis testing explores the Neyman-Pearson framework, test functions, errors, and likelihood ratio tests.

Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.

Explores Poisson processes, joint density, independence of events, and likelihood estimation.