Welcome to The Third Summer School on Statistical Methods for Linguistics and Psychology, 2019, 9-13 September

Statistical Methods for Linguistics and Psychology, University of Potsdam, Germany

Welcome to The Third Summer School on Statistical Methods for Linguistics and Psychology, 2019, 9-13 September

Application form

Please fill out this form to apply to attend this summer school. Deadling for applications: April 1, 2019. Decisions will be announced April 10, 2019.

Some seats are reserved in each stream for members of the following groups: SFB 1287, SFB 1294, and SFB 1102.

Summer School Location

Griebnitzsee Campus, University of Potsdam, Germany

The summer school will be held at the Griebnitzsee campus of the University of Potsdam; this is about 15-20 minutes away from Berlin zoo station by train. Lectures will be held in Haus 6. Invited lectures will be held in Hoersaal H02. Campus map: download from here. Please use bvg.de for planning your travel (by train or bus).

Keynote lectures

The following are confirmed speakers:

- Julia Haaf, on modeling individual differences  
- Douglas Bates, title to be announced
- Reinhold Kliegl, title to be announced
- Paul Buerkner, ordinal regression


For previous iterations of this summer school, see the website for SMLP 2017, and SMLP 2018.

Introductory frequentist statistics (maximum 30 participants)

Instructors: Daniel Schad and Audrey Buerki

Topics to be covered:

- Very basic R usage, basic probability theory, random variables (RVs),
  including jointly distributed RVs, probability distributions, 
  including bivariate distributions
- Maximum Likelihood Estimation
- sampling distribution of mean
- Null hypothesis significance testing, t-tests, confidence intervals
- type I error, type II error, power, type M and type S errors
- An introduction to (generalized) linear models
- An introduction to linear mixed models

Introductory Bayesian statistics (maximum 30 participants)

Instructors: Shravan Vasishth and Anna Laurinavichyute

Topics to be covered: course materials can be viewed here, under construction

- Basic probability theory, random variable (RV) theory, 
  including jointly distributed RVs
- probability distributions, including bivariate distributions
- Using Bayes' rule for statistical inference
- Introduction to Markov Chain Monte Carlo 
- Introduction to (generalized) linear models
- Introduction to hierarchical models
- Bayesian workflow

Advanced frequentist methods (maximum 30 participants)

Instructors: Reinhold Kliegl, Daniel Schad, Audrey Buerki, and Douglas Bates

Topics to be covered:

- Review of linear modeling theory
- Introduction to linear mixed models
- Model selection
- Contrast coding and visualizing partial fixed effects
- Shrinkage and partial pooling
- Visualization
- [If there is demand] Some new developments in linear mixed modeling in Julia

Advanced Bayesian methods (maximum 30 participants)

Instructors: Bruno Nicenboim and Shravan Vasishth

Topics will be some selection of the following topics:

- Review of basic theory
- Introduction to hierarchical modeling
- Multinomial processing trees
- Measurement error models
- Modeling censored data 
- Meta-analysis 
- Finite mixture models
- Model selection and hypothesis testing 
  (Bayes factor and k-fold cross-validation)


This summer school is funded by the DFG and is part of the SFB “Limits of Variability in Language”.