Because of the different philosophy of Bayesian statistics, where parameters are random variables and data are considered fixed, the analysis and presentation of results will differ from that of frequentist statistics. Most importantly, the probabilities that a parameter is in certain regions of the parameter space are crucial quantities in Bayesian statistics that are not calculable (or considered important) in the frequentist approach that is the basis of much of traditional statistics. In this article, I discuss the implications of these differences for presentation of the results of Bayesian analyses. In doing so, I present more detailed guidelines than are usually provided and explain the rationale for my suggestions.

由于贝叶斯统计的不同哲学，其中参数是随机变量，数据被认为是固定的，结果的分析和呈现将不同于频率统计。最重要的是，参数在参数空间的某些区域中的概率是贝叶斯统计中的关键数量，而在作为许多传统统计基础的频率论方法中，这些概率是不可计算的（或被认为是重要的）。在本文中，我将讨论这些差异对贝叶斯分析结果的呈现的影响。在此过程中，我提出了比通常提供的更详细的指导方针，并解释了我提出建议的理由。