Titre:	A Bayesian measure of model accuracy
Auteur(s):	Brunello, Gabriel Hideki Vatanabe Nakano, Eduardo Yoshio
metadata.dc.identifier.orcid:	https://orcid.org/0000-0002-9071-8512
metadata.dc.contributor.affiliation:	University of Brasília, Department of Statistics University of Brasília, Department of Statistics
Assunto::	Inferência bayesiana Qualidade de ajuste Modelo de regressão
Date de publication:	12-jui-2024
Editeur:	MDPI
Référence bibliographique:	BRUNELLO, Gabriel Hideki Vatanabe; NAKANO, Eduardo Yoshio. A Bayesian measure of model accuracy. Entropy, [S. l.], v. 26, n. 6, 510, 2024. DOI: https://doi.org/10.3390/e26060510. Disponível em: https://www.mdpi.com/1099-4300/26/6/510. Acesso em: 8 mai. 2026
Abstract:	Abstract Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model’s accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model’s predictive capability. This measure, which is straightforward and easy to understand, includes a decision criterion for model rejection. The development of this proposal adopts a Bayesian perspective of inference, elucidating the underlying concepts and outlining the necessary procedures for application. To illustrate its utility, the proposed methodology was applied to real-world data, facilitating an assessment of its practicality in real-world scenarios.
metadata.dc.description.unidade:	Instituto de Ciências Exatas (IE) Departamento de Estatística (IE EST)
metadata.dc.description.ppg:	Programa de Pós-Graduação em Estatística
Licença::	© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)
DOI:	https://doi.org/10.3390/e26060510
Collection(s) :	Artigos publicados em periódicos e afins

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