Bayesian Linear Modeling (Winter Semesters, MSc programs)
Introduction: What this course is about
This course provides an introduction to Bayesian data analysis using the probabilistic programming language Stan. We will use a front end software package called brms. This course is
for MSc Linguistics (MM5, MM6), MSc Cognitive Systems, MSc Cognitive Science.
Please see the
PULS FAQs to find out how the signup system works (in German).
We will be using the software
R,
and
RStudio,
so make sure you install these on your computer. Topics to be covered:

Basic probability theory,
random variable theory, including jointly distributed RVs,
probability distributions, including bivariate distributions
 Using Bayes' rule for statistical inference
 An introduction to (generalized) linear models
 An introduction to hierarchical models
 Measurement error models
 Mixture models
 Model selection and hypothesis testing (Bayes factor and kfold crossvalidation)
Times, location: At Golm campus, Potsdam: Seminar: Wednesdays 10:1511:45AM, II.14.009, Übung: Mondays 14:1515:45AM, II.14.009, (Haus 14 ground floor).
Lecture notes: Download from
here.
Homework: Details to be provided.
Grading: Details to be provided.
Moodle website:
All communications with students in Potsdam will be done through
this website.
Schedule
Lecture 
Topic 
Reading 
HW (solutions discussed on Mon) 
(1) Oct 15 
no class 


(2) Oct 17 + 22 
Foundations I 

HW 1 
(3) Oct 24 + 29 
Foundations II 

HW 2 
(4) Oct 31 + Nov 5 
Introduction to Bayesian data analysis I 

HW 3 
(5) Nov 7 + 12 
Introduction to Bayesian data analysis II 

HW 4 
(6) Nov 14 + 19 
Linear models I 

HW 5 
(7) Nov 21 + 26 
Linear models II 

HW 6 
(8) Nov 28 + Dec 3 
Hierarchical linear models I 

HW 7 
(9) Dec 5 + 10 
Hierarchical linear models II 

HW 8 
(10) Dec 12 + 17 
Hierarchical linear models III 

HW 9 
(11) Jan 7 
Review 


(12) Jan 9 + 14 
Measurement error models 

HW 10 
(13) Jan 16 + 21 
(Hierarchical) Mixture models 

HW 11 
(14) Jan 23 + 28 
Bayesian workflow 

HW 12 
(15) Jan 30 + Feb 4 
Model selection and hypothesis testing 

HW 13 