改进的迭代贝叶斯展开
Improved iterative Bayesian unfolding
摘要：
本文回顾了几年前发表的贝叶斯展开背后的基本思想，并改进了它们的实现。特别是，不确定性，现在处理在所有级别的概率密度函数和它们的传播是由蒙特卡洛积分。因此，较小的数字更好地处理，最终的不确定性不依赖于正态性的假设。文中还讨论了有关迭代使用算法的理论和实际问题。这个新的程序是用R语言实现的，可以免费获得，还有一些示例脚本可以用来玩玩具模型。
英文摘要：
This paper reviews the basic ideas behind a Bayesian unfolding published some years ago and improves their implementation. In particular, uncertainties are now treated at all levels by probability density functions and their propagation is performed by Monte Carlo integration. Thus, small numbers are better handled and the final uncertainty does not rely on the assumption of normality. Theoretical and practical issues concerning the iterative use of the algorithm are also discussed. The new program, implemented in the R language, is freely available, together with sample scripts to play with toy models.
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。
