Theory of Statistical Inference
This goal of the course is to give the student a solid background and understanding of main results and central methods of statistical inference, such as likelihood theory, sufficiency, information, asymptotics, resampling (bootstrap) and Bayesian statistics/aposteriori disitributions. These notions are applied to point estimation, interval estimation and hypothesis testing. Theoretical evaluations are completed with many examples and the students are trained to implement the methods of the course using statistical software.
The course consists of two elements, theory and computer exercises.
Teaching Format
Teaching is given in the form of lectures, and computer exercises.
Assessment
The course is assessed through written examination, and written presentation of the computer exercises.
Examiner
A list of examiners can be found on
Held & Bové: Likelihood and Bayesian Inference. Springer.
(Older editions of the book are called Applied Statistical Inference.)
