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Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies.

Payam Piray1, Amir Dezfouli2, Tom Heskes3

  • 1Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.

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We introduce a hierarchical Bayesian inference (HBI) framework for computational neuroscience. This method concurrently estimates parameters and compares models, improving accuracy and robustness in neuroscience research.

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Area of Science:

  • Computational Neuroscience
  • Statistical Modeling
  • Bayesian Inference

Background:

  • Traditional neuroscience research often addresses model parameter estimation and model comparison separately.
  • These two problems are interdependent, as population-level parameter inference relies on identifying which subjects express a particular model.
  • Existing methods can lead to estimation errors and biased model comparisons.

Purpose of the Study:

  • To develop a unified hierarchical Bayesian inference (HBI) framework.
  • To concurrently perform model comparison, parameter estimation, and population-level inference.
  • To address the interdependence between parameter estimation and model comparison in computational neuroscience.

Main Methods:

  • Developed a novel hierarchical Bayesian inference (HBI) framework.
  • Integrated concurrent model comparison and parameter estimation within a single Bayesian approach.
  • Utilized HBI for population-level inference, including group-level parameter testing (HBI t-test).

Main Results:

  • HBI framework demonstrated significantly smaller parameter estimation errors compared to existing methods.
  • Model comparison using HBI proved robust against outliers and unbiased towards simpler models.
  • The fully Bayesian nature of HBI facilitates robust group-level parameter inference.

Conclusions:

  • The proposed HBI framework offers substantial theoretical and experimental advantages for computational neuroscience.
  • HBI provides more accurate parameter estimation and reliable model comparison.
  • This unified approach enhances the rigor and efficiency of analyzing neuroscience data.