## 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
```

## Curriculum

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)
```

## Funding

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