    Course module   SOWBKI203  Category   B2 (Second year bachelor)  Language of instruction   English  Offered by   Radboud University; Faculty of Social Sciences; Artificial Intelligence;  Lecturer(s)     Academic year   2023   Period   SEM2  (29/01/2024 to 12/07/2024) 
 Starting block   SEM2  
 Course mode   fulltime  
 Remarks     Registration using OSIRIS   Yes  Course open to students from other faculties   Yes  Preregistration   No  Waiting list   No  Placement procedure    
     
After taking this course
 You know the three goals of statistical inference, and you can demonstrate examples of each of these.
 You understand why in Bayesian inference some parameters cannot be computed exactly, and you know when and how to use approximate solutions like Markov chain Monte Carlo.
 You can model realistic problems using (hierarchies of) common probability distributions.
 You can use probabilistic programming tools like JAGS to fit a Bayesian model to real data, and draw conclusions based on this.
 You can compare different models and find the model that best explains your data (for the Bayesian definition of ‘best’).


In science, but also in our daytoday life, we have to come to terms with the fact that we can never know everything. That means that we are inherently uncertain about the way things are – whether it is recognizing who the person across the street is, or deciding which theoretical model best describes the results of our study. When we acknowledge this uncertainty, the claims we make become accompanied by probabilities. These can either reflect the relative number of times some event occurs (e.g. the number of times a coin comes up heads or tails, divided by the number of coin flips), or our subjective belief in the event (e.g. I believe this coin lands heads up is twice as often as tails up).
The first interpretation of probability is known as frequentist statistics, and this topic will be studied in course SOWBKI138 . Here, we explore the second interpretation of probability, which is associated with Bayesian statistics. Bayesian statistics tells you how to describe and update your beliefs about the world using a simple yet powerful mathematical framework.


 
Knowledge and skills as taught in Probability Theory *SOWBKI137) and Calculus (SOWBKI104) is necessary in order to successfully pass this course.


The final grade for the Bayesian Statistics course consists of:
 50% written exam: Your exam grade must be >= 5.5.
 40% large assignment, and
 10% weekly assignments.
Please note there is only a resit opportunity for the exam.


The course grade is graded based on
1. Weekly exercises to be made in groups of 2. Average grade weighs 10% of final grade
2. Four large exercises, each weighing 10% of the final grade
3. An individual written exam, weighing 50% of the final grade




   Recommended materialsBookJohn Kruschke, Doing Bayesian data analysis, 2nd edition. Academic press / Elsevier. (Please ensure that you use the 2nd edition) 


Instructional modesAssignments RemarkThe assignments are part of the final grade
 Lecture RemarkThe course consists of one lecture a week, with some gaps to work on the large assignment.
 Practicals RemarkThere will be a weekly practical where TAs supervise groups working on weekly or larger exercise.

 TestsExamTest weight   50 
Test type   Exam 
Opportunities   Block SEM2, Block SEM2 
 AssignmentsTest weight   40 
Test type   Assignment 
Opportunities   Block SEM2 
 AssignmentTest weight   10 
Test type   Assignment 
Opportunities   Block SEM2 


  
 
 