The students learn to understand the foundations and general properties of likelihood-based statistical inference and the Bayesian approach to statistical learning including the implementation of these approaches in statistical software using appropriate numerical procedures. Topics: likelihood function and likelihood principles, maximum likelihood estimators and their properties, numerical procedures for maximum likelihood estimation, likelihood-based tests and confidence intervals (derived from Wald, score, and likelihood ratio statistics), Bootstrap, Bayes theorem, Bayes estimators and their properties, Bayesian credible intervals, prior choices, computational approaches for Bayesian inference, model choice.
StO/PO BA BWL und VWL 2016: 6 LP, Modul "Statistical Inference I"
StO/PO MA 2016: 6 LP, Modul: "Statistical Inference I"
StO/PO MEMS 2016: 6 LP, Modul: "Statistical Inference I", Major: Quantitative Methods
Wriiten exam (90 min)
Die Veranstaltung wurde 12 mal im Vorlesungsverzeichnis WiSe 2020/21 gefunden: