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The frontier of simulation-based inference.

Kyle Cranmer1,2, Johann Brehmer3,2, Gilles Louppe4

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Simulation-based inference (SBI) addresses challenges in using complex scientific simulations for data analysis. This review covers SBI

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approximate Bayesian computationimplicit modelslikelihood-free inferenceneural density estimationstatistical inference

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Area of Science:

  • Scientific modeling and computational science.
  • Interdisciplinary applications across various scientific domains.

Background:

  • Complex simulations offer high-fidelity models but are ill-suited for direct statistical inference.
  • Inverse problems arising from these simulations present significant analytical challenges.

Purpose of the Study:

  • To review the burgeoning field of simulation-based inference (SBI).
  • To identify key drivers accelerating advancements in SBI.
  • To outline the expanding frontiers and potential impact of SBI on scientific discovery.

Main Methods:

  • Review of current literature and methodologies in simulation-based inference.
  • Analysis of factors contributing to the growth of the SBI field.
  • Exploration of emerging trends and future directions in SBI.

Main Results:

  • Identification of critical challenges in applying complex simulations to statistical inference.
  • Overview of the rapidly evolving landscape of simulation-based inference techniques.
  • Highlighting the growing momentum and expanding scope of SBI.

Conclusions:

  • Simulation-based inference is a crucial development for extracting insights from complex scientific models.
  • The field is rapidly advancing, driven by new methodologies and increasing demand.
  • SBI holds profound potential to influence and accelerate scientific progress across diverse disciplines.