The number of latent factors, in factor analysis, is typically unknown and motivated by a rich literature on priors distributions, which progressively penalize the number of factors in infinite factor models. Adaptive Gibbs samplers that truncate the infinite factor models are typically used for posterior inference. In this paper, we introduce a novel strategy to adaptively truncate the number of factors that is more interpretable, stable and consistent, with respect to standard approaches.

中文翻译：

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关于无限因子模型中的截断准则

在因子分析中，潜在因子的数量通常是未知的，并且受先验分布的丰富文献的启发，这些先验分布会逐步惩罚无限因子模型中的因子数量。截断无限因子模型的自适应Gibbs采样器通常用于后验推断。在本文中，我们介绍了一种新颖的策略，可以相对于标准方法自适应地截断更多可解释，稳定和一致的因素。