论文标题
贝叶斯推断流行：线性噪声分析
Bayesian inference in Epidemics: linear noise analysis
论文作者
论文摘要
本文在设置中对贝叶斯参数推断的收敛性提供了定性的见解，该贝叶斯参数推断模仿了与相关疾病测量的疾病扩散的建模。具体而言，我们对贝叶斯模型的收敛感兴趣，并在测量限制下增加了大量数据。根据疾病测量的信息量较差，我们提供了一种“最佳情况”以及“最坏情况”分析，在前情况下，我们假设患病率是直接访问的，而在后者中，只有一个与患者相对应的二进制信号，与患者相对应检测阈值。关于真实动力学，在假定的所谓线性噪声近似下研究了这两种情况。当面对更现实的情况时，数值实验测试了我们结果的清晰度，而分析结果无法获得。
This paper offers a qualitative insight into the convergence of Bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model's convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of `best case' as well as a `worst case' analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding to a prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.
