Universidade Federal do Ceará
Mestrado e Doutorado em Ciências da Computação

Título: Bayesian Regression Models for Interval-Valued Data

Data: 27/06/2025
Horário: 14h
Local: Sala de Seminários – Bloco 952

Resumo:

Solving regression problems with interval-valued datasets is a challenging task that may arise in many real world applications. Motivated by this fact, many researchers have proposed regression methods to handle interval-valued data in recent years. Bayesian models provide a powerful framework for regression, mainly, due to their ability to lead with uncertainty. However, their application to interval data remains underexplored. In this work, we propose two Bayesian regression models for interval-valued data. The first is a linear conjugate model, while the second is an extension of Gaussian Processes designed to handle interval-valued data. Both approaches ensure that the predicted intervals are mathematically valid. In the linear case, we compare our method with two Bayesian and one non-Bayesian linear models. In the nonlinear case, we evaluate it against four state-of-the-art methods. The proposed models demonstrate competitive performance, in addition to ensuring valid interval predictions and providing a quantification of uncertainty.

Banca examinadora:

• Prof. Dr. César Lincoln Cavalcante Mattos (MDCC/UFC) – Orientador
• Prof. Dr. João Paulo Pordeus Gomes (UFC) – Coorientador
• Prof. Dr. João Paulo do Vale Madeiro (UFC)
• Prof. Dr. Ricardo Coelho Silva (UFC)