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Summary

This study introduces methods to improve simulator-based inference by augmenting training data for surrogate models. These techniques enhance the efficiency and quality of extracting information from complex simulations.

Keywords:
implicit modelsneural density estimationsimulation-based inference

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Area of Science:

  • Computational Science
  • Statistical Inference
  • Machine Learning

Background:

  • Simulators offer realistic models but often yield intractable probability densities, complicating inverse problems.
  • Surrogate models, such as normalizing flows and density ratio estimators, are used to approximate these intractable densities.
  • Existing methods face challenges in sample efficiency and inference quality.

Purpose of the Study:

  • To enhance simulator-based inference by leveraging additional information from the latent process.
  • To improve the training of surrogate models for intractable probability densities.
  • To boost sample efficiency and the quality of inference in complex simulations.

Main Methods:

  • Extracting auxiliary information characterizing the latent process from simulators.
  • Augmenting training data for surrogate models using this extracted information.
  • Developing and applying novel loss functions that utilize augmented data.

Main Results:

  • Demonstrated improvement in sample efficiency for surrogate model training.
  • Showcased enhanced quality of inference through augmented data.
  • Validated the effectiveness of proposed loss functions in improving inference.

Conclusions:

  • Augmenting surrogate model training data with latent process information significantly improves inference.
  • The proposed loss functions offer a practical approach to enhance simulator-based inference.
  • These advancements are crucial for tackling complex inverse problems in scientific modeling.