A Review of Bayesian Methods for Infinite Factorisations
Methodology
2023-09-25 v1
Abstract
Defining the number of latent factors has been one of the most challenging problems in factor analysis. Infinite factor models offer a solution to this problem by applying increasing shrinkage on the columns of factor loading matrices, thus penalising increasing factor dimensionality. The adaptive MCMC algorithms used for inference in such models allow to defer the dimension of the latent factor space automatically based on the data. This paper presents an overview of Bayesian models for infinite factorisations with some discussion on the properties of such models as well as their comparative advantages and drawbacks.
Cite
@article{arxiv.2309.12990,
title = {A Review of Bayesian Methods for Infinite Factorisations},
author = {Margarita Grushanina},
journal= {arXiv preprint arXiv:2309.12990},
year = {2023}
}
Comments
20 pages