论文标题
符号发现的贝叶斯实验设计
Bayesian Experimental Design for Symbolic Discovery
论文作者
论文摘要
这项研究涉及贝叶斯最佳实验设计与符号发现的配方和应用，这是从预测模型采用一般功能形式的观察数据中的推断。我们使用限制的一阶方法来优化适当的选择标准，并使用哈密顿蒙特卡洛（Hamiltonian Monte Carlo）从先验中取样。计算涉及卷积的预测分布的步骤是通过数值集成或快速变换方法计算的。
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.
