使用swyft进行模拟-高效的边际后验估计：停止浪费您的宝贵时间
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
摘要：
我们提出的算法（a）嵌套神经似然证据比估计，和（b）模拟重用通过非均匀泊松点过程缓存的参数和相应的模拟。总之，这些算法使自动和极其模拟器有效的边缘和联合后验估计。该算法适用于广泛的物理和天文学问题，通常提供一个数量级更好的模拟器效率比传统的基于似然的采样方法。我们的方法是一个例子的似然自由推理，因此它也适用于模拟器不提供一个听话的似然函数。模拟器运行永远不会被拒绝，并且可以在未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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
