A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes

TL;DR

This paper introduces a Bayesian estimation method for analyzing non-Gaussian data-generating processes, specifically addressing challenges posed by latent classes in LiNGAM models.

Contribution

It proposes a novel Bayesian estimation approach that effectively handles latent classes in non-Gaussian data models, improving upon existing LiNGAM methods.

Findings

The new method reduces bias caused by latent classes.

It extends LiNGAM to a Bayesian framework.

Demonstrates improved estimation accuracy.

Abstract

A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data. A linear non-Gaussian acyclic model (LiNGAM) based on the non-Gaussianity of external influences has been proposed to estimate the data-generating processes of variables. However, the results of the estimation can be biased if there are latent classes. In this paper, we first review LiNGAM, its extended model, as well as the estimation procedure for LiNGAM in a Bayesian framework. We then propose a new Bayesian estimation procedure that solves the problem.