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Optimal Bayesian design for model discrimination via classification.

Markus Hainy1,2, David J Price3,4,5, Olivier Restif5

  • 1Department of Applied Statistics, Johannes Kepler University, 4040 Linz, Austria.

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Summary
This summary is machine-generated.

This study introduces a novel supervised classification method for efficient Bayesian optimal design in model discrimination. The approach significantly reduces computational demands compared to traditional methods, enhancing model selection processes.

Keywords:
Approximate Bayesian computationBayesian model selectionClassification and regression treeContinuous-time Markov processRandom forestSimulation-based Bayesian experimental design

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Area of Science:

  • Statistics
  • Computational Statistics
  • Machine Learning

Background:

  • Optimal Bayesian design for model discrimination is crucial but computationally intensive.
  • Existing methods often require extensive simulations, especially with complex likelihood functions.
  • Intractable likelihoods further complicate the design process.

Purpose of the Study:

  • To develop a computationally efficient approach for Bayesian optimal model discrimination design.
  • To reduce the number of simulations required compared to approximate Bayesian computation methods.
  • To provide an easy assessment of design performance using misclassification error rates.

Main Methods:

  • Utilizing supervised classification methods for Bayesian optimal design.
  • Comparing the new approach with existing simulation-intensive methods.
  • Evaluating performance through misclassification error rates.

Main Results:

  • The supervised classification approach requires considerably fewer simulations.
  • This method offers computational advantages for models with both tractable and intractable likelihoods.
  • Performance assessment via misclassification error rate is straightforward.

Conclusions:

  • Supervised classification provides an efficient alternative for Bayesian optimal model discrimination design.
  • The method is particularly beneficial for complex models with computationally expensive or intractable likelihoods.
  • This advancement simplifies and accelerates the design process for model discrimination.