论文标题
当统计模拟前向模拟时，强大的后推断
Robust posterior inference when statistically emulating forward simulations
论文作者
论文摘要
科学分析通常依赖于缓慢但准确的正向模型来根据已知模型参数进行的可观察数据。尽管存在各种仿真方案来近似这些缓慢的计算，但仅当近似值得到充分理解和控制时，这些方法才安全。该研讨会提交的审查和更新了一种先前发布的方法，该方法已在宇宙学模拟中使用，以（1）训练模拟器，同时使用MCMC估算后验概率，（2）将模拟误差转化为模型参数后验概率的错误。我们演示了如何将这些技术应用于$λ$ CDM宇宙学模型的参数的快速估算后验分布，同时还测量了模拟器近似的稳健性。
Scientific analyses often rely on slow, but accurate forward models for observable data conditioned on known model parameters. While various emulation schemes exist to approximate these slow calculations, these approaches are only safe if the approximations are well understood and controlled. This workshop submission reviews and updates a previously published method, which has been used in cosmological simulations, to (1) train an emulator while simultaneously estimating posterior probabilities with MCMC and (2) explicitly propagate the emulation error into errors on the posterior probabilities for model parameters. We demonstrate how these techniques can be applied to quickly estimate posterior distributions for parameters of the $Λ$CDM cosmology model, while also gauging the robustness of the emulator approximation.
