Tomazella, Vera Lucia DamascenoVera Lucia DamascenoTomazellaJesus, Sandra RegoSandra RegoJesusGazon, Amanda BuosiAmanda BuosiGazonLouzada, FranciscoFranciscoLouzadaNadarajah, SaraleesSaraleesNadarajahNascimento, Diego CarvalhoDiego CarvalhoNascimentoRodrigues, Francisco AparecidoFrancisco AparecidoRodriguesRamos, Pedro LuizPedro LuizRamos2025-12-302025-12-3020212073-8994https://hdl.handle.net/20.500.12740/23932This article proposes the use of the Bayesian reference analysis to estimate the parameters of the generalized normal linear regression model. It is shown that the reference prior led to a proper posterior distribution, while the Jeffreys prior returned an improper one. The inferential purposes were obtained via Markov Chain Monte Carlo (MCMC). Furthermore, diagnostic techniques based on the Kullback-Leibler divergence were used. The proposed method was illustrated using artificial data and real data on the height and diameter of Eucalyptus clones from Brazil.Acceso AbiertoBayesian inferencegeneralized normal linear regression modelnormal linear regression modelreference priorJeffreys priorKullback– Leibler divergencehttp://dx.doi.org/10.3390/sym13050856