Horseshoe-type Priors for Independent Component Estimation
Independent Component Estimation (ICE) has many applications in modern day machine learning as a feature engineering extraction method. Horseshoe-type priors are used to provide scalable algorithms 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 and deep learning. We also discuss how to implement conditional posteriors and envelope-based methods for optimization. Through this hierarchy representation, we unify a number of hitherto disparate estimation procedures. We illustrate our methodology and algorithms on a numerical example. Finally, we conclude with directions for future research.
Code (0)
등록된 구현이 없습니다.
Tasks
Feature EngineeringSimilar Papers 제목 키워드 기반
False Discovery Rate Control via Frequentist-assisted Horseshoe
The horseshoe prior, a widely used handy alternative to the spike-and-slab prior, has proven to be an exceptional default global-local shrinkage prior in Bayesian inference and machine learning. However, designing tests …
Bayesian InferenceSemi-parametric Expert Bayesian Network Learning with Gaussian Processes and Horseshoe Priors
This paper proposes a model learning Semi-parametric rela- tionships in an Expert Bayesian Network (SEBN) with linear parameter and structure constraints. We use Gaussian Pro- cesses and a Horseshoe prior to introduce mi…
Gaussian ProcessesSparse Horseshoe Estimation via Expectation-Maximisation
The horseshoe prior is known to possess many desirable properties for Bayesian estimation of sparse parameter vectors, yet its density function lacks an analytic form. As such, it is challenging to find a closed-form sol…
FormSparse Estimation with Generalized Beta Mixture and the Horseshoe Prior
In this paper, the use of the Generalized Beta Mixture (GBM) and Horseshoe distributions as priors in the Bayesian Compressive Sensing framework is proposed. The distributions are considered in a two-layer hierarchical m…
Compressive SensingHorseshoe Priors for Spatial Small Area Estimation: Regular Variation, Tail Robustness, and Deep Learning
Small area estimation borrows strength across domains to repair the poor precision of direct survey estimators. Two philosophies dominate the area-level literature. The first, descending from Ghosh and Rao (1994), borrow…