论文标题
近似贝叶斯对1、2、3和4类型的相互作用推断，并应用于疾病映射
Approximate Bayesian Inference for the Interaction Types 1, 2, 3 and 4 with Application in Disease Mapping
论文作者
论文摘要
我们在本文中解决了一种新的方法，该方法将时空模型与使用1,2,3类型的相互作用类型和4相拟合在疾病映射中的应用。但是，当位置数量和/或时间点的数量较大时，由于推理所需的大量所需约束数量，推理在计算上具有挑战性，这对于包括Markov Chain Monte Carlo（MCMC）和集成的嵌套Laplace近似（INLA）在内的各种推理体系结构（包括）。我们根据密度矩阵重新构建INLA方法，以拟合具有四种相互作用类型的固有时空模型，并考虑了总和到零约束，并讨论了如何在高性能计算框架中实现新方法。使用新方法的计算时间不取决于约束的数量，并且在现实情况下，与INLA相比，可以达到40倍的速度。通过仿真研究和真实的数据应用程序验证了这种方法，并在R inlaplus和Python标头函数中实现了：Inla1234（）。
We address in this paper a new approach for fitting spatiotemporal models with application in disease mapping using the interaction types 1,2,3, and 4. When we account for the spatiotemporal interactions in disease-mapping models, inference becomes more useful in revealing unknown patterns in the data. However, when the number of locations and/or the number of time points is large, the inference gets computationally challenging due to the high number of required constraints necessary for inference, and this holds for various inference architectures including Markov chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximations (INLA). We re-formulate INLA approach based on dense matrices to fit the intrinsic spatiotemporal models with the four interaction types and account for the sum-to-zero constraints, and discuss how the new approach can be implemented in a high-performance computing framework. The computing time using the new approach does not depend on the number of constraints and can reach a 40-fold faster speed compared to INLA in realistic scenarios. This approach is verified by a simulation study and a real data application, and it is implemented in the R package INLAPLUS and the Python header function: inla1234().
