تقرير
Bayesian Deep ICE
| العنوان: | Bayesian Deep ICE |
|---|---|
| المؤلفون: | Datta, Jyotishka, Polson, Nicholas G. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Methodology, Computer Science - Machine Learning, 62F15, 62H25, 68T07 |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2406.17058Test |
| رقم الانضمام: | edsarx.2406.17058 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |