<p>Prior distributions.</p>

Accurate, reliable, and efficient estimation of blood glucose dynamics from real-world data is challenging due to the time-varying nature, high uncertainty, and nonlinear interplay of complex processes. In this study, we propose and investigate a stochastic representation of a virtual population by fitting a hierarchical Bayesian model. In total, we use 500 24h-long sequences, 50 from each of the 10 patients with type 1 diabetes on multiple daily injection therapy. We model uncertainty on multiple levels, in physiology and in self-reported events, and take into account intra- and interday variability, and the effect of physical activity as well. The root-mean-square error between the glucose measurements and the mean of the posterior predictive distribution using the fitted low-rank multivariate normal guide is 12.44 mg/dL. We show that the posterior distributions can be used to simulate realistic intra-, and interday variability in terms of the investigated patient cohort.从真实世界数据中精准、可靠且高效地估算血糖动态变化颇具挑战，这是由于复杂生理过程具备时变特性、高度不确定性以及非线性交互作用。本研究通过拟合分层贝叶斯模型（hierarchical Bayesian model），提出并探究了虚拟人群的随机表征方案。本次研究共采用500条时长为24小时的序列数据，10名接受每日多次注射治疗的1型糖尿病患者各提供50条序列。我们从生理层面与自我报告事件层面构建多维度不确定性模型，同时纳入日内与日间变异性以及身体活动的影响因素。采用拟合得到的低秩多元正态引导分布（low-rank multivariate normal guide）计算所得的血糖测量值与后验预测分布（posterior predictive distribution）均值间的均方根误差（root-mean-square error）为12.44 mg/dL。研究证实，后验分布可在所研究的患者队列中模拟出贴合实际的日内与日间变异性特征。