A Computationally More Efficient Bayesian Approach for Estimating Continuous-Time Models

Continuous-time modeling is gaining in popularity as more and more intensive longitudinal data need to be analyzed. Current Bayesian software implementations of continuous-time models suffer from rather high, inadequate run times. Therefore, we apply a model reformulation approach to reduce run time. In a simulation study, we investigate the estimation quality and run time gain. We then illustrate our optimized Bayesian continuous-time model estimation and compare it to established continuous-time modeling software using an empirical example. Parameter estimates and inference statistics were very comparable, while run times were very different. Our approach reduces the run times for Bayesian estimations of continuous-time models from hours to minutes.连续时间建模（Continuous-time modeling）的应用热度与日俱增，因当前需分析的密集型纵向数据日益增多。现有针对连续时间建模的贝叶斯（Bayesian）软件实现普遍存在运行时长过长、效率不达标的问题。为此，我们采用模型重构方法以缩减计算时长。我们通过模拟实验，评估了该方法的估计质量与计算时长优化效果。随后，我们借助一则实证案例，演示了优化后的贝叶斯连续时间模型估计流程，并将其与成熟的连续时间建模软件进行对比。参数估计结果与推断统计量高度相近，但计算时长却存在显著差异。我们的方法可将连续时间模型的贝叶斯估计运行时长从数小时大幅缩短至数分钟。