Horseshoe-type Priors for Independent Component Estimation

Autor:	Datta, Jyotishka, Polson, Nicholas G.
Rok vydání:	2024
Předmět:	Statistics - Methodology Computer Science - Machine Learning 62F15 62H25 68T07
Druh dokumentu:	Working Paper
Popis:	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. Comment: 23 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.17058 Zobrazit plný text záznamu View this record from Arxiv