cosmoabc：基于总体蒙特卡罗近似贝叶斯计算的无似然推理
cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation
摘要：
近似贝叶斯计算（ABC）可以在真实似然函数未知、不可用或计算成本太高的情况下对复杂物理系统进行参数推断。它依赖于模拟数据的正演模拟和观测星表与合成星表之间的比较。在这里，我们介绍了cosmoabc，一个Python ABC采样器，具有原始ABC算法的人口蒙特卡罗（PMC）变体，它使用自适应重要性采样方案。代码非常灵活，可以很容易地耦合到外部模拟器，同时允许合并任意距离和先验函数。作为实际应用的一个例子，我们耦合cosmoabc与numcosmo库，并演示了它如何可以用来估计后验概率分布的宇宙学参数的基础上测量的星系团数量计数，而无需计算的似然函数。cosmoabc在PyPI和GitHub上根据GPL v3许可证发布，文档可在http://goo.gl/SmB8EX上获得
英文摘要：
Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX
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。
