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Lecture
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 Graph Search.
This lecture covers the Cramér-Rao bound, asymptotic efficiency, and hypothesis testing in statistical theory. It explains the Fisher information, optimality in decision theory, and the Neyman-Pearson setup. The lecture delves into the asymptotic normality of the Maximum Likelihood Estimator (MLE) and the concept of point estimation for parametric families.