Cited by 385Statistical Methods and Bayesian InferenceControl Systems and IdentificationGaussian Processes and Bayesian Inference

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VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

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Daunizeau, Jean, Adam, Vincent, Rigoux, Lionel·2014·PLoS Computational Biology

DOI 10.1371/journal.pcbi.1003441PMC PMC3900378

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Abstract

This work is in line with an on-going effort tending toward a computational (quantitative and refutable) understanding of human neuro-cognitive processes. Many sophisticated models for behavioural and neurobiological data have flourished during the past decade. Most of these models are partly unspecified (i.e. they have unknown parameters) and nonlinear. This makes them difficult to peer with a formal statistical data analysis framework. In turn, this compromises the reproducibility of model-based empirical studies. This work exposes a software toolbox that provides generic, efficient and robust probabilistic solutions to the three problems of model-based analysis of empirical data: (i) data simulation, (ii) parameter estimation/model selection, and (iii) experimental design optimization.

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Authors: Daunizeau, J., Adam, V., & Rigoux, L.
Affiliations: Gatsby Computational Neuroscience Unit (GB)
Institut du Cerveau (FR)
ORCID: Adam, Vincent
Rigoux, Lionel
Venue: PLoS Computational Biology
Publisher: Public Library of Science (PLoS)
Published: 2014-01-23
Volume: 10
Issue: 1
Pages: e1003441
ISSN: 1553-734X (ISSN-L)
1553-7358
DOI: 10.1371/journal.pcbi.1003441
PMC: PMC3900378
OpenAlex: W2049657715
Cited by: 385
References: 41
Citations by year: 2026: 28 · 2025: 36 · 2024: 44 · 2023: 37 · 2022: 39 · 2021: 47 · 2020: 47 · 2019: 24 · 2018: 22 · 2017: 24 · 2016: 21 · 2015: 7 · 2014: 9
FWCI: 21.23
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: PLoS Computational Biology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Figshare (submittedVersion, cc-by)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
Keywords: stochastic modeling, nonlinear models, parameter estimation, model selection, data simulation, experimental design optimization, behavioral data, computational neuroscience, reproducibility
MeSH terms: Algorithms, Bayes Theorem, Cognition, Computer Simulation, Decision Making, Humans, Models, Biological, Models, Neurological, Nerve Net, Probability, Software, Stochastic Processes, Normal Distribution, Computational Biology

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VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data

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