论文标题
改善MCMC的性能，用于最接近的邻居高斯过程模型，并具有完整的数据增强
Improving performances of MCMC for Nearest Neighbor Gaussian Process models with full data augmentation
论文作者
论文摘要
即使最近的邻居高斯工艺（NNGP）大大减轻了MCMC的贝叶斯时空模型的实施，但它们并不能解决高模型维度引起的收敛问题。节俭的替代方案（例如响应或崩溃算法）是一个答案。我们的方法是保持完整的数据增强，但要试图使其更有效。我们提出了两种策略。第一个方案是特别关注模型看似微不足道的固定效果。我们从经验上表明，重新介绍截距上的潜在领域可以改善链行为。我们将这种方法扩展到可能干扰连贯的空间场的其他固定效应。我们提出了一种简单的方法，由于NNGP的稀疏性，因此不需要调整，同时保持负担得起。第二个方案使用色度采样器加速了随机场的采样。该方法使长顺序模拟归结为与组平行或组向前的采样。因此，可以将平行NNGP可能性并行的有吸引力的可能性转移到场采样中。我们介绍了公共存储库中高斯字段的方法https://github.com/sebastiencoube/improving_nngp_full_augmentation。提供了广泛的小插图。我们在两个合成玩具示例以及最先进的软件包SPNNGP的状态下运行实施。最后，我们将方法应用于美利坚合众国大陆的真实数据集。
Even though Nearest Neighbor Gaussian Processes (NNGP) alleviate considerably MCMC implementation of Bayesian space-time models, they do not solve the convergence problems caused by high model dimension. Frugal alternatives such as response or collapsed algorithms are an answer.gree Our approach is to keep full data augmentation but to try and make it more efficient. We present two strategies to do so. The first scheme is to pay a particular attention to the seemingly trivial fixed effects of the model. We show empirically that re-centering the latent field on the intercept critically improves chain behavior. We extend this approach to other fixed effects that may interfere with a coherent spatial field. We propose a simple method that requires no tuning while remaining affordable thanks to NNGP's sparsity. The second scheme accelerates the sampling of the random field using Chromatic samplers. This method makes long sequential simulation boil down to group-parallelized or group-vectorized sampling. The attractive possibility to parallelize NNGP likelihood can therefore be carried over to field sampling. We present a R implementation of our methods for Gaussian fields in the public repository https://github.com/SebastienCoube/Improving_NNGP_full_augmentation . An extensive vignette is provided. We run our implementation on two synthetic toy examples along with the state of the art package spNNGP. Finally, we apply our method on a real data set of lead contamination in the United States of America mainland.
