论文标题
仿真有效的边缘后验估计与Swyft：停止浪费宝贵的时间
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
论文作者
论文摘要
我们提出了嵌套神经可能性与证据比率估计的算法（a），以及（b）通过参数的不均匀泊松点过程缓存和相应的模拟。这些算法一起，可以对边缘和关节后代的自动和极其模拟器的有效估计。该算法适用于广泛的物理和天文学问题，通常比基于传统的可能性采样方法提供的模拟器效率更好。我们的方法是无似然推理的一个示例，因此它也适用于不提供可拖动可能性函数的模拟器。模拟器运行永远不会被拒绝，可以在将来的分析中自动重复使用。作为功能原型实现，我们提供了开源软件包Swyft。
We present algorithms (a) for nested neural likelihood-to-evidence ratio estimation, and (b) for simulation reuse via an inhomogeneous Poisson point process cache of parameters and corresponding simulations. Together, these algorithms enable automatic and extremely simulator efficient estimation of marginal and joint posteriors. The algorithms are applicable to a wide range of physics and astronomy problems and typically offer an order of magnitude better simulator efficiency than traditional likelihood-based sampling methods. Our approach is an example of likelihood-free inference, thus it is also applicable to simulators which do not offer a tractable likelihood function. Simulator runs are never rejected and can be automatically reused in future analysis. As functional prototype implementation we provide the open-source software package swyft.
