The Use of Variational Inference for Lifetime Data with Spatial Correlations

TL;DR

This paper explores the application of variational inference as a computationally efficient alternative to MCMC for Bayesian analysis of spatial lifetime data, demonstrating its effectiveness on real datasets.

Contribution

It introduces variational inference methods with different divergence metrics for spatial lifetime models, addressing computational challenges in large datasets.

Findings

VI methods offer faster computation than MCMC.

VI achieves comparable estimation accuracy to MCMC.

Application to real data demonstrates practical advantages.

Abstract

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.