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Parallel probability density approximation

Citation

Lin, YS and Heathcote, A and Holmes, WR, Parallel probability density approximation, Behavior Research Methods, 51 pp. 2777-2799. ISSN 1554-3528 (2019) [Refereed Article]


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Copyright Statement

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

Official URL: https://holmeslabhome.files.wordpress.com/2018/11/...

DOI: doi:10.3758/s13428-018-1153-1

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:R, C++, CUDA, GPU, kernel density estimate, Markov Chain Monte Carlo
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Machine learning not elsewhere classified
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in psychology
UTAS Author:Lin, YS (Dr Yi-Shin Lin)
UTAS Author:Heathcote, A (Professor Andrew Heathcote)
ID Code:129174
Year Published:2019
Web of Science® Times Cited:2
Deposited By:Psychology
Deposited On:2018-11-13
Last Modified:2020-05-01
Downloads:1 View Download Statistics

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