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A Tactile Automated Passive-Finger Stimulator (TAPS)
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Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems.

Tina Toni1, David Welch, Natalja Strelkowa

  • 1Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK. ttoni@imperial.ac.uk

Journal of the Royal Society, Interface
|February 11, 2009
PubMed
Summary
This summary is machine-generated.

Approximate Bayesian computation with sequential Monte Carlo (ABC SMC) estimates dynamical model parameters without likelihoods. This method also aids in model selection and parameter inference for biological systems.

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Area of Science:

  • Computational Biology
  • Statistical Modeling
  • Bayesian Inference

Background:

  • Approximate Bayesian computation (ABC) offers a way to estimate posterior distributions when likelihood calculation is intractable.
  • Sequential Monte Carlo (SMC) methods are powerful tools for dynamic systems and Bayesian inference.

Purpose of the Study:

  • To apply and evaluate an ABC method based on SMC for parameter estimation in dynamical models.
  • To assess the utility of ABC SMC for understanding parameter inferability and model sensitivity.
  • To develop ABC SMC as a tool for Bayesian model selection.

Main Methods:

  • Sequential Monte Carlo Approximate Bayesian Computation (ABC SMC) algorithm.
  • Parameter estimation for dynamical systems.
  • Bayesian model selection framework.

Main Results:

  • ABC SMC successfully estimates parameters and credible intervals for biological systems.
  • The method provides insights into parameter inferability and model sensitivity.
  • ABC SMC outperforms other ABC approaches and effectively selects the best model among competing descriptions.

Conclusions:

  • ABC SMC is a versatile and effective tool for parameter estimation in dynamical models.
  • This approach enhances understanding of model behavior and aids in model selection.
  • The developed ABC SMC method offers a robust Bayesian framework for complex biological systems.