Reginatto M
Physikalisch-Technische Bundesanstalt, D-38116 Braunschweig, Germany.
Radiat Prot Dosimetry. 2006;121(1):64-9. doi: 10.1093/rpd/ncl096. Epub 2006 Jul 28.
Bayesian methods provide a unified framework for combining information in the presence of uncertainty. All uncertainties that enter into the description of a measurement are modelled using probability distributions, and these are handled according to the rules of probability theory, ensuring that the approach is free of inconsistencies. The final result of the analysis is the full probability distribution for the parameter of interest, and from this distribution an appropriate uncertainty interval can be obtained. Some of the advantages of a Bayesian analysis include a straightforward approach to the problem of dealing with nuisance parameters, the ability to incorporate prior information in a natural way and the flexibility that is necessary for a realistic modelling of the measurement process. As an example, the problem of deriving neutron dose estimates and their uncertainties based on measurements carried out using a Bonner sphere spectrometer is considered.
贝叶斯方法提供了一个在存在不确定性的情况下组合信息的统一框架。所有进入测量描述的不确定性都使用概率分布进行建模,并根据概率论的规则进行处理,确保该方法不存在不一致性。分析的最终结果是感兴趣参数的完整概率分布,并且可以从该分布中获得适当的不确定区间。贝叶斯分析的一些优点包括处理干扰参数问题的直接方法、以自然方式纳入先验信息的能力以及对测量过程进行现实建模所需的灵活性。作为一个例子,考虑了基于使用邦纳球谱仪进行的测量来推导中子剂量估计及其不确定性的问题。