ArticleJournal of mathematical psychology2019
Thermodynamic Integration and Steppingstone Sampling Methods for Estimating Bayes Factors: A Tutorial.
Article in Journal of mathematical psychology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A two-phase diffusion process account of cross-location conflict processing during mental imagery.iScience · 2026Article
- An introduction to thermodynamic integration and application to dynamic causal models.Cognitive neurodynamics · 2022Review
- Preregistration in diverse contexts: a preregistration template for the application of cognitive models.Royal Society open science · 2021Article
- Assessing the practical differences between model selection methods in inferences about choice response time tasks.Psychonomic bulletin & review · 2019Article
- Optimal or not; depends on the task.Psychonomic bulletin & review · 2019Article
- Thermodynamic integration via differential evolution: A method for estimating marginal likelihoods.Behavior research methods · 2019Article
- Bayesian statistical approaches to evaluating cognitive models.Wiley interdisciplinary reviews. Cognitive science · 2018Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
One of the more principled methods of performing model selection is via Bayes factors. However, calculating Bayes factors requires marginal likelihoods, which are integrals over the entire parameter space, making estimation of Bayes factors for models with more than a few parameters a significant computational challenge. Here, we provide a tutorial review of two Monte Carlo techniques rarely used in psychology that efficiently compute marginal likelihoods:
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