University Of Tasmania
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Parallel probability density approximation

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journal contribution
posted on 2023-05-19, 22:29 authored by Lin, Y, Heathcote, A, Holmes, WR
Probability Density Approximation (PDA) is a non-parametric method of calculating probability densities. When integrated into Bayesian estimation, it allows researchers to fit psychological processes for which analytic probability functions are unavailable, significantly expanding the scope of theories that can be quantitatively tested. PDA is, however, computationally intensive, requiring large numbers of Monte Carlo simulations to attain good precision. We introduce Parallel PDA (pPDA), a highly efficient implementation of this method utilizing Armadillo C++ and CUDA C libraries to conduct millions of model simulations simultaneously in graphics processing units (GPUs). This approach provides a practical solution for rapidly approximating probability densities with high precision. In addition to demonstrating this method, we fit a Piecewise Linear Ballistic Accumulator model (Holmes, Trueblood & Heathcote, 2016) to empirical data. Finally, we conduct simulation studies to investigate various issues associated with the PDA and provide guidelines for pPDA applications to other complex cognitive models.


Publication title

Behavior Research Methods








School of Psychological Sciences


Springer New York LLC

Place of publication

United States

Rights statement

Copyright The Psychonomic Society, Inc. 2019 Post-prints are subject to Springer Nature re-use terms

Repository Status

  • Restricted

Socio-economic Objectives

Expanding knowledge in psychology

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    University Of Tasmania