ABSTRACT 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.

中文翻译：

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一种用于估计连续时间模型的计算效率更高的贝叶斯方法

摘要 随着需要分析越来越密集的纵向数据，连续时间建模越来越受欢迎。当前连续时间模型的贝叶斯软件实现受到相当长的运行时间不足的影响。因此，我们应用模型重构方法来减少运行时间。在模拟研究中，我们调查了估计质量和运行时间增益。然后，我们说明我们优化的贝叶斯连续时间模型估计，并使用经验示例将其与已建立的连续时间建模软件进行比较。参数估计和推理统计数据非常具有可比性，而运行时间却大不相同。我们的方法将连续时间模型的贝叶斯估计的运行时间从几小时减少到几分钟。