Deep Independent Component Estimation (DICE) has many applications in modern
day machine learning as a feature engineering extraction method. We provide a
novel latent variable representation of independent component analysis that
enables both point estimates via expectation-maximization (EM) and full
posterior sampling via Markov Chain Monte Carlo (MCMC) algorithms. Our
methodology also applies to flow-based methods for nonlinear feature
extraction. We discuss how to implement conditional posteriors and
envelope-based methods for optimization. Through this representation hierarchy,
we unify a number of hitherto disjoint estimation procedures. We illustrate our
methodology and algorithms on a numerical example. Finally, we conclude with
directions for future research.

深度独立分量估计（DICE）是一种现代机器学习中用于特征工程提取的方法，通过期望最大化（EM）和马尔可夫链蒙特卡罗（MCMC）算法，我们提供了独立分量分析的新型潜变量表示，该方法还适用于非线性特征提取的流体方法，并讨论了如何实现条件后验和基于包络的优化方法，通过这种表示层次，我们统一了许多迄今为止不相关的估计过程，在数值实例上说明了我们的方法和算法，最后，我们总结了未来研究方向。