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This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms. We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltonian Monte Carlo to sample from the prior. A step for computing the predictive distribution, involving convolution, is computed via either numerical integration, or via fast transform methods.

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Bayesian Experimental Design for Symbolic Discovery

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Kenneth L. Clarkson,
  • Cristina Cornelio,
  • Sanjeeb Dash,
  • Joao Goncalves,
  • Lior Horesh,
  • Nimrod Megiddo

摘要

This study concerns the formulation and application of Bayesian optimal experimental design to symbolic discovery, which is the inference from observational data of predictive models taking general functional forms. We apply constrained first-order methods to optimize an appropriate selection criterion, using Hamiltonian Monte Carlo to sample from the prior. A step for computing the predictive distribution, involving convolution, is computed via either numerical integration, or via fast transform methods.