Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.
This lecture covers the comparison of two simple hypotheses, Neyman-Pearson lemma, rejection regions, optimal tests, probabilities, likelihood, maximum likelihood estimation, observed and expected information, and the construction of confidence intervals. It also discusses the likelihood ratio statistic, limit distribution of the maximum likelihood estimator, and the elements of a statistical hypothesis test. The instructor explains the importance of p-values, power of tests, and decision-making based on statistical evidence.