HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python

@inproceedings{Wiecki2013HDDMHB,
  title={HDDM: Hierarchical Bayesian estimation of the Drift-Diffusion Model in Python},
  author={Thomas V. Wiecki and Imri Sofer and Michael J. Frank},
  booktitle={Front. Neuroinform.},
  year={2013}
}
The diffusion model is a commonly used tool to infer latent psychological processes underlying decision-making, and to link them to neural mechanisms based on response times. Although efficient open source software has been made available to quantitatively fit the model to data, current estimation methods require an abundance of response time measurements to recover meaningful parameters, and only provide point estimates of each parameter. In contrast, hierarchical Bayesian parameter estimation… CONTINUE READING
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HDDM: Hierarchical Bayesian estimation of the DriftDiffusion Model in Python

  • TV Citation Wiecki, I Sofer, MJ Frank
  • 2013

Wiecki, Sofer and Frank. This is an open-access article distributed under the terms of the Creative

  • Neuroinform
  • 2013

Hierarchical Bayesian parameter estimation for cumulative prospect theory

  • H. k. Nilsson, J. Rieskamp, E.-J. Wagenmakers
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