Shravan Vasishth

Professor, Dept. of Linguistics, University of Potsdam, 14476 Potsdam, Germany
Speaker, Language Cluster, Cognitive Science
Phone: +49-(0)331-977-2950 | Fax: - 2087 | Email: vasishth at uni-potsdam.de
GPG public key, Orcid ID, google scholar, github, bitbucket, statistics blog, vasishth lab blog
Doing a PhD with me: README.1st
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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 sign-up 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:
  1. Basic probability theory, random variable theory, including jointly distributed RVs, probability distributions, including bivariate distributions
  2. Using Bayes' rule for statistical inference
  3. An introduction to (generalized) linear models
  4. An introduction to hierarchical models
  5. Measurement error models
  6. Mixture models
  7. Model selection and hypothesis testing (Bayes factor and k-fold cross-validation)
Times, location: At Golm campus, Potsdam: Seminar: Wednesdays 10:15-11:45AM, II.14.009, Übung: Mondays 14:15-15:45AM, 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