标题： 用于具有空间相关性的寿命数据的变分推断方法 显示英文标题

标题： The Use of Variational Inference for Lifetime Data with Spatial Correlations

摘要： 具有空间相关性的寿命数据在现代工程、临床和医疗应用中经常被收集用于分析。 对于这种空间寿命数据，统计模型通常通过空间随机效应来考虑空间依赖性，例如累积暴露模型和比例风险模型。 对于这些模型，贝叶斯估计常用于模型推断，但当空间位置数量较大时，常常遇到计算挑战。 传统的马尔可夫链蒙特卡洛（MCMC）方法在抽样后验分布时可能耗时较长。 在本案例研究论文中，我们研究变分推断（VI）在空间寿命数据模型推断中的能力，旨在实现估计精度和计算效率之间的良好平衡。 具体而言，研究了不同散度度量的VI方法在空间寿命模型中的应用。 在案例研究中，使用了Titan GPU寿命数据和松树寿命数据，从计算优势和估计精度的角度检验VI方法。 显示英文摘要

摘要： Lifetime data with spatial correlations are often collected for analysis in modern engineering, clinical, and medical applications. For such spatial lifetime data, statistical models usually account for the spatial dependence through spatial random effects, such as the cumulative exposure model and the proportional hazards model. For these models, the Bayesian estimation is commonly used for model inference, but often encounters computational challenges when the number of spatial locations is large. The conventional Markov Chain Monte Carlo (MCMC) methods for sampling the posterior can be time-consuming. In this case-study paper, we investigate the capability of variational inference (VI) for the model inference on spatial lifetime data, aiming for a good balance between the estimation accuracy and computational efficiency. Specifically, the VI methods with different divergence metrics are investigated for the spatial lifetime models. In the case study, the Titan GPU lifetime data and the pine tree lifetime data are used to examine the VI methods in terms of their computational advantage and estimation accuracy.