Web8. dec 2024 · Most of previous works and applications of Bayesian factor model have assumed the normal likelihood regardless of its validity. We propose a Bayesian factor model for heavy-tailed high-dimensional... Web1. máj 2024 · We work within a Bayesian framework and pursue the parametric approach of Lucas et al. (2006). We adjust the specification to a dynamic factor model with a sparse factor loading matrix. Sparsity is induced by specifying a point mass–normal mixture prior distribution for the factor loadings, which assigns a positive probability to zero.

Infinite Sparse Factor Analysis and Infinite Independent Components …

Web1. jún 2011 · Factor models aim to explain the dependence structure among high-dimensional observations ... WebWe propose a Bayesian factor model for heavy-tailed high-dimensional data based on multivariate Student-tlikelihood to obtain better covariance estimation. We use … difference between article 132 and 133

Nonparametric Bayesian sparse factor models with application to …

WebA nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially … WebWe focus on sparse modeling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative gamma process shrinkage prior on the … WebSparse Bayesian infinite factor models We focus on sparse modelling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative … forge of empires play online