Nonparametric Bayesian modeling for non-normal data through a transformation

In many applications, modeling based on a normal kernel is preferred because not only does the normal kernel belong to the family of stable distributions, but also it is easy to satisfy the stationary condition in the stochastic process. However, the characteristic of the data, such as count or prop...

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Bibliographic Details

Published in:	AIMS Mathematics
Main Authors:	Sangwan Kim, Yongku Kim, Jung-In Seo
Format:	Article
Language:	English
Published:	AIMS Press 2024-05-01
Subjects:	blocked gibbs sampler dirichlet process mixture nonparametric bayesian transformation transformed bernstein polynomial
Online Access:	https://www.aimspress.com/article/doi/10.3934/math.2024883?viewType=HTML