Horseshoe-type Priors for Independent Component Estimation

Music City Center 

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 for fast implementation. Our method 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.

Bayesian, Structure learning, high-dimensional.