论文标题
Cobaya：贝叶斯分析层次物理模型的代码
Cobaya: Code for Bayesian Analysis of hierarchical physical models
论文作者
论文摘要
我们提出了Cobaya，这是一种通用贝叶斯分析代码，该代码针对具有复杂内部相互依赖的模型。在不需要用户的特定代码的情况下，模型管道的不同阶段之间的相互依赖性被利用以进行抽样效率：中间结果会自动缓存，并且根据其依赖关系将参数分组为块，并最佳地分类，并考虑到他们的个人计算成本，以最大程度地降低其在Sampled and Algorith中的成本。 Cobaya允许使用一系列Monte Carlo Samplers探索后期，并且具有具有新的先验和可能性的蒙特卡洛样品的最大化和重要性培养功能。 Cobaya以Python的形式编写，该模块化方式允许可扩展性，使用外部软件包提供的计算以及动态重新聚体化而无需修改其源。它可以利用混合openMP/MPI并行化，并且每个后验评估具有亚毫秒的开销。尽管Cobaya是一个通用统计框架，但它包括与一组宇宙学玻尔兹曼代码和可能性的接口（后者相对于前者的选择不可知），以及用于外部依赖性的自动安装程序。
We present Cobaya, a general-purpose Bayesian analysis code aimed at models with complex internal interdependencies. Without the need for specific code by the user, interdependencies between different stages of a model pipeline are exploited for sampling efficiency: intermediate results are automatically cached, and parameters are grouped in blocks according to their dependencies and optimally sorted, taking into account their individual computational costs, so as to minimize the cost of their variation during sampling, thanks to a novel algorithm. Cobaya allows exploration of posteriors using a range of Monte Carlo samplers, and also has functions for maximization and importance-reweighting of Monte Carlo samples with new priors and likelihoods. Cobaya is written in Python in a modular way that allows for extendability, use of calculations provided by external packages, and dynamical reparameterization without modifying its source. It can exploit hybrid OpenMP/MPI parallelization, and has sub-millisecond overhead per posterior evaluation. Though Cobaya is a general purpose statistical framework, it includes interfaces to a set of cosmological Boltzmann codes and likelihoods (the latter being agnostic with respect to the choice of the former), and automatic installers for external dependencies.
