论文标题
高斯正交的潜在因子过程，用于大型相关数据的不完整矩阵
Gaussian orthogonal latent factor processes for large incomplete matrices of correlated data
论文作者
论文摘要
我们引入了高斯正交潜在因子过程，以建模和预测大型相关数据。为了应对计算挑战，我们首先用 一个多维输入域中的正交成分处的密度较低的产物。实施连续的卡尔曼滤波器以有效地计算了可能性函数而无需进行近似。我们还表明，由于因子过程和正交因子加载矩阵的事先独立，因子过程的后验分布是独立的。对于具有较大样本量的研究，我们提出了一种灵活的方式来建模平均值，并得出边缘后验分布以解决这些参数时解决可识别性问题。模拟和实际数据应用程序都证实了此方法的出色性能。
We introduce Gaussian orthogonal latent factor processes for modeling and predicting large correlated data. To handle the computational challenge, we first decompose the likelihood function of the Gaussian random field with a multi-dimensional input domain into a product of densities at the orthogonal components with lower-dimensional inputs. The continuous-time Kalman filter is implemented to compute the likelihood function efficiently without making approximations. We also show that the posterior distribution of the factor processes is independent, as a consequence of prior independence of factor processes and orthogonal factor loading matrix. For studies with large sample sizes, we propose a flexible way to model the mean, and we derive the marginal posterior distribution to solve identifiability issues in sampling these parameters. Both simulated and real data applications confirm the outstanding performance of this method.
