论文标题
在累积收缩过程中，高斯因子模型的变分贝叶斯
Variational Bayes for Gaussian Factor Models under the Cumulative Shrinkage Process
论文作者
论文摘要
累积收缩过程是越来越多的收缩率，可以在模型中使用，在这种模型中，其他术语应该逐渐忽略不计。自然的应用是高斯因子模型，在该模型中，这种过程已证明有效地诱导了简约的表示，同时对数据协方差矩阵提供了准确的推断。累积收缩过程带有一个自适应Gibbs采样器，该采样器调整了整个迭代中的潜在因素的数量，这使其比非自适应Gibbs采样器更快。在这项工作中，我们提出了一种具有累积收缩过程的高斯因子模型的变异算法。这种策略提供了关于自适应吉布斯采样器的可比推断，并进一步降低了运行时
The cumulative shrinkage process is an increasing shrinkage prior that can be employed within models in which additional terms are supposed to play a progressively negligible role. A natural application is to Gaussian factor models, where such a process has proved effective in inducing parsimonious representations while providing accurate inference on the data covariance matrix. The cumulative shrinkage process came with an adaptive Gibbs sampler that tunes the number of latent factors throughout iterations, which makes it faster than the non-adaptive Gibbs sampler. In this work we propose a variational algorithm for Gaussian factor models endowed with a cumulative shrinkage process. Such a strategy provides comparable inference with respect to the adaptive Gibbs sampler and further reduces runtime
