Bayesian Methods
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- Tutorials
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- Bayesian Methods
As the use of large data sets and complex modeling methods becomes more common in psychological science, students and researchers must be equipped with the relevant statistical knowledge.
In particular, Bayesian statistical methods have become increasingly popular, both within cognitive psychology and more broadly in the field. Bayesian methods enable new ways of integrating uncertainty, prior knowledge, and beliefs into models. They provide a more expansive way of working with psychological data, which are often inherently subjective and susceptible to incompleteness.
This set of tutorials aims to introduce readers to major concepts in Bayesian analysis, as well as prepare them to apply Bayesian methods in their own work. The tutorials make use of real psychological data sets and model affective, cognitive, and social phenomena.
To reference these tutorials, cite:
Fischer, J. R. (2024). Bayesian methods tutorials for psychological research [Undergraduate honors thesis, Stanford University].
To reference the AMIB data, which two of the tutorials make use of, cite:
Ram, N., Conroy, D. E., Pincus, A. L., Hyde, A. L., & Molloy, L. E. (2012). Tethering theory to method: Using measures of intraindividual variability to operationalize individuals’ dynamic characteristics. In G. Hancock & J. Harring (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 81-110). New York: Information Age.
To reference the response time data, which one of the tutorials makes use of, cite:
Gates, V., Callaway, F., Ho, M. K., & Griffiths, T. L. (2021). A rational model of people’s inferences about others’ preferences based on response times. Cognition, 217. https://doi.org/10.1016/j.cognition.2021.104885
Prerequisites:
- Familiarity with probability and statistics at the introductory college level
- E.g., Stanford CS 109—course reader courtesy of Chris Piech
- Proficiency in the R programming language
| Topic | Tutorial | |
|---|---|---|
| 1 | Bayesian Basics | The Basics of the Bayesian Approach: An Introductory Tutorial |
| 2 | Bayesian Parameter Estimation | Modeling With Uncertainty: A Bayesian Parameter Estimation Tutorial |
| 3 | Bayesian Networks | Stochastic Systems: A Bayesian Networks Tutorial |
| 4 | Bayesian Cognitive Modeling | The Probabilistic Mind: A Bayesian Cognitive Modeling Tutorial |
