论文标题
快速贝叶斯推断的并行化集成的嵌套拉普拉斯近似
Parallelized integrated nested Laplace approximations for fast Bayesian inference
论文作者
论文摘要
源于更大的数据可用性和较高维度的模型参数空间，对执行大规模的贝叶斯推理任务的需求不断增长。在这项工作中，我们为集成的嵌套拉普拉斯近似方法（INLA）提供了并行化策略，这是对潜在高斯模型类别进行近似贝叶斯推断的流行框架。我们的方法利用了嵌套的OpenMP并行性，这是一种使用INLA优化阶段和最先进的稀疏线性求解器Pardiso的平行线路搜索过程。我们利用算法中的相互独立函数评估以及先进的稀疏线性代数技术。这样，我们可以灵活地利用当今多核体系结构的力量。我们在许多不同的现实世界应用程序上演示了新的并行化方案的性能。引入并行性会导致所有较大模型的速度10及更多。我们的工作已经集成到当前版本的开源R-Inla软件包中，使其改进的性能方便地可供所有用户使用。
There is a growing demand for performing larger-scale Bayesian inference tasks, arising from greater data availability and higher-dimensional model parameter spaces. In this work we present parallelization strategies for the methodology of integrated nested Laplace approximations (INLA), a popular framework for performing approximate Bayesian inference on the class of Latent Gaussian models. Our approach makes use of nested OpenMP parallelism, a parallel line search procedure using robust regression in INLA's optimization phase and the state-of-the-art sparse linear solver PARDISO. We leverage mutually independent function evaluations in the algorithm as well as advanced sparse linear algebra techniques. This way we can flexibly utilize the power of today's multi-core architectures. We demonstrate the performance of our new parallelization scheme on a number of different real-world applications. The introduction of parallelism leads to speedups of a factor 10 and more for all larger models. Our work is already integrated in the current version of the open-source R-INLA package, making its improved performance conveniently available to all users.
