ArticlePLoS computational biology2019
Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers.
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Who cites it
79 citing papers in PubMed, 142 citations in OpenAlex.
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- Anchored and Propagated Updating Within Pseudoscientific Belief Systems.Annals of the New York Academy of Sciences · 2026Article
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- Addressing low statistical power in computational modelling studies in psychology and neuroscience.Nature human behaviour · 2026Article
- Computational and neural mechanisms underlying the influence of action affordances on value learning.Nature communications · 2026Article
- Neuroeconomic adaptation to norm shifts is preserved in borderline personality disorder.Acta neuropsychiatrica · 2026Article
- Computational modeling of rhythmic expectations: Perspectives, pitfalls, and prospects.PLoS computational biology · 2025Review
- Global, regional, and national bladder cancer burden from 1990 to 2021 and forecasts to 2050: a systematic analysis of the global disease burden study in 2021.BMC public health · 2025Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
Abstract
Computational modeling plays an important role in modern neuroscience research. Much previous research has relied on statistical methods, separately, to address two problems that are actually interdependent. First, given a particular computational model, Bayesian hierarchical techniques have been used to estimate individual variation in parameters over a population of subjects, leveraging their population-level distributions. Second, candidate models are themselves compared, and individual variation in the expressed model estimated, according to the fits of the models to each subject. The interdependence between these two problems arises because the relevant population for estimating parameters of a model depends on which other subjects express the model. Here, we propose a hierarchical Bayesian inference (HBI) framework for concurrent model comparison, parameter estimation and inference at the population level, combining previous approaches. We show that this framework has important advantages for both parameter estimation and model comparison theoretically and experimentally. The parameters estimated by the HBI show smaller errors compared to other methods. Model comparison by HBI is robust against outliers and is not biased towards overly simplistic models. Furthermore, the fully Bayesian approach of our theory enables researchers to make inference on group-level parameters by performing HBI t-test.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.