Knowledge:

1. Basic theoretical knowledge about fundamental principles for statistical inference

2. knowledge about construction of interval estimators, and hypothesis testing; 

3. The evaluation of these estimators and tests. 

4. Insight in how to construct optimal estimators and tests. 

5. Difference between Frequentist and Bayesian inference. 

6. Knowledge of obtaining Bayesian estimators.

Skills:

1. Ability to develop theoretical arguments.. 
2. Obtaining a deeper understanding and a considerable extension to the statistical inference theory in the bachelor courses.

3.Ability to perform point estimation, hypothesis testing and interval estimation under a large variety of discrete and continuous probability models.

4. Ability to evaluate the properties of these estimators and tests, for both finite sample sizes and 
asymptotically as the sample size tends to infinity. 

5. Ability to differentiate frequentisty and Bayesian inference.