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Selecting summary statistics in approximate Bayesian computation for calibrating stochastic models.

Tom Burr1, Alexei Skurikhin

  • 1Statistical Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.

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Summary
This summary is machine-generated.

Approximate Bayesian computation (ABC) calibrates computer models using summary statistics. Choosing effective statistics is crucial for accurate parameter estimation in biological models, as demonstrated with mitochondrial DNA data.

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Area of Science:

  • Computational Biology
  • Statistical Modeling
  • Population Genetics

Background:

  • Approximate Bayesian computation (ABC) is widely used for calibrating stochastic computer models, especially in biology.
  • ABC is favored for large datasets or high-dimensional parameters due to its reliance on summary statistics, enhancing computational feasibility.
  • A key challenge in ABC is that the accuracy of parameter posterior distribution estimation depends heavily on the choice of summary statistics.

Purpose of the Study:

  • To investigate the impact of user-selected summary statistics on the accuracy of Approximate Bayesian computation.
  • To illustrate the importance of effective summary statistics using a mitochondrial DNA population dynamics model.
  • To propose a strategy for selecting optimal summary statistics in ABC.

Main Methods:

  • Utilized Approximate Bayesian computation (ABC) for model calibration.
  • Employed a stochastic computer model and real data from mitochondrial DNA population dynamics.
  • Evaluated the performance of different sets of summary statistics in approximating the posterior distribution of model parameters.

Main Results:

  • Demonstrated that the choice of summary statistics significantly affects the accuracy of the posterior distribution approximation.
  • Showed that certain summary statistics yield close approximations, while others result in poor approximations.
  • Identified a strategy for selecting effective summary statistics when the model can be run at multiple parameter settings.

Conclusions:

  • The selection of summary statistics is a critical determinant of success in Approximate Bayesian computation.
  • Effective summary statistics are essential for accurate parameter estimation in complex biological models.
  • The proposed strategy offers guidance for improving ABC analyses in similar applications.