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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 Maximum Likelihood Estimation (MLE) method in statistical inference, focusing on its properties and applications. Topics include the consistency and asymptotic properties of MLE, the bias and variance of estimators, the Cramér-Rao bound, and the method of moments. The instructor discusses the challenges in finding explicit solutions for MLE in certain distributions, such as the Cauchy and Gamma distributions, and introduces the Newton-Raphson iteration and the method of moments as alternative estimation techniques.