Open AccessBayesian Perspective in the Selection of Bean Genotypes
Author(s) -
Tâmara Rebecca Albuquerque de Oliveira,
Moysés Nascimento,
Paulo Renato Z. Santos,
Kleyton Danilo S. Costa,
Thalyson Vasconcelos Lima,
Gabriela Karoline Michelon,
L. C. de Faria,
Antônio Félix da Costa,
José Wilson da Silva,
G.A. Gravina,
Gustavo Hugo Ferreira de Oliveira
Publication year - 2020
Publication title -
journal of agricultural science
Language(s) - English
Resource type - Journals
eISSN - 1916-9760
pISSN - 1916-9752
DOI - 10.5539/jas.v12n9p173
Subject(s) - frequentist inference , prior probability , bayesian probability , a priori and a posteriori , statistics , mathematics , bayesian inference , stability (learning theory) , prior information , adaptability , computer science , artificial intelligence , machine learning , biology , philosophy , ecology , epistemology
Changes in the relative performance of genotypes have made it necessary for more in-depth investigations to be carried out through reliable analyses of adaptability and stability. The present study was conducted to compare the efficiency of different informative priors in the Bayesian method of Eberhart & Russel with frequentist methods. Fifteen black-bean genotypes from the municipalities of Belém do São Francisco and Petrolina (PE, Brazil) were evaluated in 2011 and 2012 in a randomized-block design with three replicates. Eberhart & Russel’s methodology was applied using the GENES software and the Bayesian procedure using the R software through the MCMCregress function of the MCMCpack package. The quality of Bayesian analysis differed according to the a priori information entered in the model. The Bayesian approach using frequentist analysis had greater accuracy in the estimate of adaptability and stability, where model 1 which uses the a priori information, was the most suitable to obtain reliable estimates according to the BayesFactor function. The inference, using information from previous studies, showed to be imprecise and equivalent to the linear-model methodology. In addition, it was realized that the input of a priori information is important because it increases the quality of the adjustment of the model.