论文标题
几乎相互正交过程的贝叶斯建模
Bayesian modeling of nearly mutually orthogonal processes
论文作者
论文摘要
功能因素分析是功能和纵向数据的重要降低方法。因子负载可深入了解观测值的可变性模式，而潜在因素为数据提供了低维表示，这对于推论任务有用。将功能因子负荷限制为相互正交的，对于模型放映是可取的，但在计算上具有挑战性。在这项工作中，我们介绍了几乎相互正交的过程，这些过程可用于有效地强制对因子负载的相互正交性，同时保持计算简单性和效率。联合分布受惩罚参数的控制，该惩罚参数确定过程是相互正交的，并且与后验计算的易于性有关。我们证明，我们的方法可用于研究母乳喂养状况，疾病和人口统计学因素对幼儿体重动态的影响，用于灵活和可解释的推断。代码可在github上找到：https：//github.com/jamesmatuk/nemo-ffa
Functional factor analysis is an important dimension reduction method for functional and longitudinal data. Factor loadings give insight into patterns of variability of the observations, while latent factors provide a low-dimensional representation of the data that is useful for inferential tasks. Constraining the functional factor loadings to be mutually orthogonal is desirable for model parsimony, but is computationally challenging. In this work, we introduce nearly mutually orthogonal processes, which can be used to effectively enforce mutual orthogonality of the factor loadings, while maintaining computational simplicity and efficiency. The joint distribution is governed by a penalty parameter that determines the degree to which the processes are mutually orthogonal and is related to ease of posterior computation. We demonstrate that our approach can be used for flexible and interpretable inference in an application to studying the effects of breastfeeding status, illness, and demographic factors on weight dynamics in early childhood. Code is available on GitHub: https://github.com/jamesmatuk/NeMO-FFA
