ArticlePsychometrika2014
Hierarchical approximate Bayesian computation.
Article in Psychometrika, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Article
- Probing the origins of subjective confidence in source memory decisions in young and older adults: A sequential sampling account.Journal of experimental psychology. General · 2025Article
- Article
- A Modeling Framework to Examine Psychological Processes Underlying Ordinal Responses and Response Times of Psychometric Data.Psychometrika · 2023Article
- The quest for simplicity in human learning: Identifying the constraints on attention.Cognitive psychology · 2022Article
- Efficient Bayesian inference for stochastic agent-based models.PLoS computational biology · 2022Article
- Intraobject and extraobject memory binding across early development.Developmental psychology · 2022Article
- Transparency, replicability, and discovery in cognitive aging research: A computational modeling approach.Psychology and aging · 2022Article
- Individual Differences in Cue Weighting in Sentence Comprehension: An Evaluation Using Approximate Bayesian Computation.Open mind : discoveries in cognitive science · 2022Article
- Article
- On the Neural and Mechanistic Bases of Self-Control.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Some task demands induce collapsing bounds: Evidence from a behavioral analysis.Psychonomic bulletin & review · 2018Article
- Bayesian statistical approaches to evaluating cognitive models.Wiley interdisciplinary reviews. Cognitive science · 2018Review
- Testing the validity of conflict drift-diffusion models for use in estimating cognitive processes: A parameter-recovery study.Psychonomic bulletin & review · 2018Article
- Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.BMC medical genomics · 2017Article
- Strategies for improving approximate Bayesian computation tests for synchronous diversification.BMC evolutionary biology · 2017Article
- Approaches to Analysis in Model-based Cognitive Neuroscience.Journal of mathematical psychology · 2017Article
- A generalized, likelihood-free method for posterior estimation.Psychonomic bulletin & review · 2014Review
- Likelihood-free Bayesian analysis of memory models.Psychological review · 2013Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model's parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.
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