| Inhalte: |
The seminar will cover all necessary steps of analyzing behavioral data using mathematical models with Bayesian inference. You learn basic concepts like probability distributions, parameter inference, parameter recovery, and model selection. We start with inferring means, and the weights of a linear regression model with Bayesian inference, before we fit behavioral data from a classical task with a cognitive model. At the end of the seminar, you can analyze your own data with a simple Bayesian model. For the analyses we use Python with PyMC3. You don’t need prior knowledge of Python or PyMC3, but you should already have CAN6 completed. The graded seminar paper consists of the creation of a model and data analysis using the model for a data set that is provided by the seminar instructor.
Key Literature:
Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547.
Martin, O. A., Kumar, R., & Lao, J. (2022). Bayesian Modeling and Computation in Python. Chapman and Hall/CRC.
Lee, M. D., & Wagenmakers, E. J. (2014). Bayesian cognitive modeling: A practical course. Cambridge university.
Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature human behaviour, 5(10), 1282-1291.
|
