用Dempster-Shafer分析估计Poisson计数数据的极限
Estimating limits from Poisson counting data using Dempster--Shafer analysis
摘要：
我们提出了一个Dempster-Shafer（DS）的方法来估计极限的泊松计数数据与滋扰参数。Dempster-Shafer是一个统计框架，概括了贝叶斯统计。DS演算通过允许质量分布在事件空间的幂集上来增强传统的概率。这消除了对先验分布的贝叶斯依赖，同时允许在先验信息可用时并入先验信息。我们使用Poisson Dempster-Shafer模型（DSM）推导出“Banff上限挑战”三泊松模型的后验DSM。结果与其他方法相比，显示出该方法的实用性。我们认为，减少依赖先验提供的Dempster-谢弗框架是实际和理论上可取的。
英文摘要：
We present a Dempster--Shafer (DS) approach to estimating limits from Poisson counting data with nuisance parameters. Dempster--Shafer is a statistical framework that generalizes Bayesian statistics. DS calculus augments traditional probability by allowing mass to be distributed over power sets of the event space. This eliminates the Bayesian dependence on prior distributions while allowing the incorporation of prior information when it is available. We use the Poisson Dempster--Shafer model (DSM) to derive a posterior DSM for the ``Banff upper limits challenge'' three-Poisson model. The results compare favorably with other approaches, demonstrating the utility of the approach. We argue that the reduced dependence on priors afforded by the Dempster--Shafer framework is both practically and theoretically desirable.
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。
