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Computing Bayes Factors for Evidence-Accumulation Models Using Warp-III Bridge Sampling

Gronau, QF, Heathcote, A ORCID: 0000-0003-4324-5537 and Matzke, D 2019 , 'Computing Bayes Factors for Evidence-Accumulation Models Using Warp-III Bridge Sampling' , Behavior Research Methods , doi:

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Over the last decade, the Bayesian estimation of evidence-accumulation models has gainedpopularity, largely due to the advantages afforded by the Bayesian hierarchical framework.Despite recent advances in the Bayesian estimation of evidence-accumulation models,model comparison continues to rely on suboptimal procedures, such as posterior parameterinference and model selection criteria known to favor overly complex models. In this paperwe advocate model comparison for evidence-accumulation models based on the Bayesfactor obtained via Warp-III bridge sampling. We demonstrate, using the Linear BallisticAccumulator (LBA), that Warp-III sampling provides a powerful and flexible approachthat can be applied to both nested and non-nested model comparisons, even in complexand high-dimensional hierarchical instantiations of the LBA. We provide an easy-to-usesoftware implementation of the Warp-III sampler and outline a series of recommendationsaimed at facilitating the use of Warp-III sampling in practical applications.

Item Type: Article
Authors/Creators:Gronau, QF and Heathcote, A and Matzke, D
Keywords: bayesian model comparison, Differential Evolution Markov Chain Monte Carlo, dynamic models of choice, linear ballistic accumulator, marginal likelihood, response time models
Journal or Publication Title: Behavior Research Methods
Publisher: Springer New York LLC
ISSN: 1554-3528
DOI / ID Number:
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Copyright the Author(s) 2019. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)

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