https://github.com/lacerbi/bamb2022-model-fitting

Tutorials on statistical model fitting (optimization, Bayesian inference) for Day 2 of BAMB! 2022.
https://github.com/lacerbi/bamb2022-model-fitting

Last synced: 23 days ago
JSON representation

Tutorials on statistical model fitting (optimization, Bayesian inference) for Day 2 of BAMB! 2022.

• Host: GitHub
• URL: https://github.com/lacerbi/bamb2022-model-fitting
• Owner: lacerbi
• License: mit
• Created: 2022-08-23T07:35:22.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-09-02T15:06:58.000Z (about 2 years ago)
• Last Synced: 2023-03-02T15:21:50.413Z (over 1 year ago)
• Language: MATLAB
• Homepage:
• Size: 93.9 MB
• Stars: 20
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Introduction to Optimization and Bayesian Inference for Model Fitting

![bamb-logo](https://github.com/lacerbi/bamb2022-model-fitting/blob/main/figs/bamb-logo.png?raw=true)

Tutorials on statistical model fitting (optimization and Bayesian inference) for Day 2 of the [BAMB! 2022 summer school](https://www.bambschool.org/) for advanced modeling of behavior. The provided tutorials are in MATLAB, but the content is language-independent.

**Lecturer:** [Luigi Acerbi](https://www.helsinki.fi/en/researchgroups/machine-and-human-intelligence), [@AcerbiLuigi](https://twitter.com/AcerbiLuigi) (University of Helsinki).

- To run the tutorials, download / clone the repository locally.

- Ensure that the BADS and VBMC toolboxes are installed (see below).

- **Introduction to Optimization for Statistical Model fitting:** [slides](acerbi-optimization-BAMB-sep2022.pdf), [code](bamb2022_optimization_tutorial.m).

- **Introduction to Bayesian Inference for Statistical Model fitting:** [slides](acerbi-bayes-BAMB-sep2022.pdf), [code](bamb2022_bayes_tutorial.m).

- **Visualization of optimization algorithms:** https://github.com/lacerbi/optimviz

### Toolboxes for model fitting

The tutorials use the following open-source MATLAB toolboxes. You will need to install them from here:

- *Bayesian Adaptive Direct Search* (BADS) optimization algorithm: https://github.com/lacerbi/bads

- *Variational Bayesian Monte Carlo* (VBMC) inference algorithm: https://github.com/lacerbi/vbmc

### License

Unless stated otherwise, the material in this repo is released under the [MIT License](LICENSE).