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Behavioural Analysis, Bayesian

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Encyclopedia of Computational Neuroscience

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Definition

Bayesian analysis is a method for reasoning probabilistically about parameters of a model given some observed data. Applied to behavioral data, Bayesian analysis can be used to fit and compare models of cognition. This approach to behavioral data analysis offers a number of advantages over classical (frequentist) analysis, including a coherent representation of uncertainty, flexibility to handle complex models and missing data, and an avoidance of pathologies inherent to significance testing based on p-values.

Detailed Description

Bayesian analysis starts with the specification of a joint distribution on the data D and parameters (or hidden variables) H. This joint distribution can be broken down into two components: P(D,H) = P(D|H) P(H). The first component, P(D|H), is known as the likelihood, and the second component, P(H), is known as the prior. Given some data, we are interested in the conditional distribution, P(H|D), commonly known as the posterior; this distribution...

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References

  • Kass RE, Raftery AE (1995) Bayes factors. J Am Stat Assoc 90:773–795

    Article  Google Scholar 

  • Kruschke JK (2010) Doing Bayesian data analysis: a tutorial introduction with R and BUGS. Academic, Burlington

    Google Scholar 

  • Lee MD (2011) How cognitive modeling can benefit from hierarchical Bayesian models. J Math Psychol 55:1–7

    Article  Google Scholar 

  • Robert CP, Casella G (2004) Monte Carlo statistical methods. Springer, New York

    Book  Google Scholar 

  • Vanpaemel W (2010) Prior sensitivity in theory testing: an apologia for the Bayes factor. J Math Psychol 54:491–498

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Samuel J. Gershman

Authors
  1. Samuel J. Gershman
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Corresponding author

Correspondence to Samuel J. Gershman .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Gershman, S.J. (2014). Behavioural Analysis, Bayesian. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_446-1

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_446-1

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4614-7320-6

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

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