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Efficient estimation of bounded gradient-drift diffusion models for affect on CPU and GPU.

Tim Loossens1, Kristof Meers2, Niels Vanhasbroeck2

  • 1KU LEUVEN, Tiensestraat 102 - bus 3713, 3000, Leuven, Belgium. tim.loossens@kuleuven.be.

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Summary
This summary is machine-generated.

This study introduces a Julia package for fitting the Affective Ising Model (AIM), a non-linear computational model for affect dynamics. The package offers fast likelihood computations on GPUs/CPUs, overcoming estimation challenges.

Keywords:
Affect dynamicsAffective Ising ModelCPUEuler-MaruyamaGPUMetropolis-HastingsNon-linear diffusion models

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Area of Science:

  • Computational neuroscience
  • Affective computing
  • Statistical modeling

Background:

  • Continuous-time stochastic models are increasingly used in affect research.
  • The Affective Ising Model (AIM) is a novel non-linear model for affect dynamics.
  • Non-linear models like AIM present computational challenges in parameter estimation due to complex likelihood functions.

Purpose of the Study:

  • To introduce a Julia software package for efficient parameter estimation of the Affective Ising Model (AIM).
  • To implement a fast numerical algorithm for computing the AIM's likelihood function on GPUs and CPUs.
  • To address the computational challenges associated with fitting non-linear, continuous-time stochastic models in affect research.

Main Methods:

  • Development of a Julia package implementing a novel numeric algorithm for fast likelihood computations.
  • Comparison of the new numerical method with the traditional Euler-Maruyama method for solving stochastic differential equations.
  • Performance benchmarking on various computing devices (GPUs and CPUs) and a parameter recovery study.

Main Results:

  • The Julia package enables rapid computation of the AIM's likelihood function.
  • Parameter estimation using the new method is significantly faster than traditional approaches.
  • A single parameter estimation can be achieved in under thirty seconds on a mainstream NVIDIA GPU.

Conclusions:

  • The developed Julia package significantly enhances the computational feasibility of using the Affective Ising Model (AIM) in affect research.
  • The optimized numerical algorithm and GPU/CPU implementation overcome major hurdles in parameter estimation for non-linear stochastic models.
  • This work facilitates broader application of advanced computational models in understanding affect dynamics.