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.

翻译：本文从质量上深入了解巴伊西亚参数推论的趋同性,这种推论模仿一种模式,模仿一种疾病传播的模型和相关的疾病测量。具体地说,我们对巴伊西亚模型与测量限制下越来越多的数据趋同感兴趣,视疾病测量资料不足的情况而定,我们提供了一种“最佳案例”以及一种“最坏案例”分析,在前一种情况下,我们假设这种流行率可以直接获得,而在后一种情况下,我们假设只有与流行性检测阈值相对应的二元信号。两种案例都是在假定的所谓线性噪音近似真实动态的情况下研究的。数量实验检验我们面对无法取得分析结果的更现实的情况时的结果的锐性。