Course main content
- Estimators: bias, variance. Consistency, bias-variance decomposition.
- Likelihood and maximum likelihood estimators. Exponential models.
- Fisher information, Fréchet-Darmois-Cramer-Rao bound, asymptotic normality, delta-method.
- Asymptotic properties of the maximum likelihood estimators. Associated tests.
- Concentration inequalities
- Bayesian statistics (a priori and a posteriori distributions, maximum a posteriori estimator), variational and EM algorithms
- Enseignant: Emilie Kaufmann
- Enseignant: Hemant Tyagi