发表机构
Universität Heidelberg; University of Tübingen; ELLIS Institute Tübingen; Zuse School ELIZA（海德堡大学; 蒂宾根大学; 蒂宾根ELLIS研究所; 祖塞ELIZA学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出马尔可夫链蒙特卡洛循环类比热机循环，通过自适应集成调度器实现参数调整，发现非高斯模型可产生非零净功，并作为非高斯性度量应用于超新星宇宙学。
AI 中文摘要
本文提出了马尔可夫链蒙特卡洛（MCMC）循环的概念，类比热机中的循环过程，用以研究贝叶斯推断问题。为此，我们开发了自适应集成调度器，能够在MCMC运行过程中调整贝叶斯正则系综的外部参数，从而在实践中实现MCMC循环。我们在不同的统计模型上运行这些循环。作为一项基本洞见，我们从理论和实践两方面发现，当且仅当所考虑的模型是非高斯分布时，此类系统才能产生非零的净功输出。因此，它们可以作为贝叶斯推断中非高斯性的度量，我们将其应用于超新星宇宙学的一个实例进行测试。
英文摘要
The concept of Markov chain Monte Carlo (MCMC) cycles, an analogy to cyclic processes in heat engines, is presented in order to examine Bayesian inference problems. In this effort, we develop adaptive ensemble schedulers that allow the tuning of external parameters of a Bayesian canonical ensemble during an MCMC run, realising the MCMC cycles in practice. We run these cycles on different statistical models. As a fundamental insight, we find (both theoretically and in practice) that such systems can produce a non-zero net work output if and only if the considered model is non-Gaussian. As such, they may serve as a measure of non-Gaussianity in Bayesian inference, which we test on an example from supernova cosmology.
