The selection of progenies in breeding programs can generate great advances when associated with Bayesian inference because it allows the incorporation of a priori knowledge. Selection at self plant level is advantageous when evaluating half-sib progenies. However, it becomes very difficult for sweet potato cultivation are expected to support the improvement of sweet potato populations. In this context, BLUPIS (best linear unbiased prediction individual simulated) becomes an good alternative technique. Therefore, this study aimed to use the a priori knowledge obtained in previous sweet potato experiments through Bayesian inference to estimate genetic parameters and gains from selection and afterwards to choose the better half-sib progenies considering the BLUPIS. Sixteen progenies were evaluated for root and branch yield, root shape, and resistance to soil insects. The data were analyzed using Bayesian theory, considering data from 12 previous experiments to obtain the informative a priori. All variables tested, as total root yield, commercial root yield, branch green mass yield, average weight of commercial roots, root shape e resistance to soil insects showed high values for coefficient of heritability. Expressive gains are expected to support the improvement of sweet potato populations. This applied methodology, will be allowed breeders to re-design and select the most promising progenies during all breeding process for improve well determined and specific traits in sweet potato.

当与贝叶斯推理相关联时，育种程序中后代的选择可以产生巨大的进步，因为它允许结合先验知识。在评估半同胞后代时，自体植物水平的选择是有利的。然而，红薯种植很难支持红薯种群的提高。在这种情况下，BLUPIS（最佳线性无偏预测个体模拟）成为一种很好的替代技术。因此，本研究旨在使用先验通过贝叶斯推理在先前的甘薯实验中获得的知识来估计遗传参数和从选择中获得的收益，然后再考虑 BLUPIS 来选择更好的半同胞后代。对 16 个后代的根和枝产量、根形状和对土壤昆虫的抗性进行了评估。使用贝叶斯理论分析数据，考虑来自 12 个先前实验的数据以获得信息先验. 测试的所有变量，如总根产量、商品根产量、分支绿色质量产量、商品根的平均重量、根形状和对土壤昆虫的抗性，均显示出较高的遗传系数值。预计表达性收益将支持红薯种群的改善。这种应用方法将允许育种者在所有育种过程中重新设计和选择最有前途的后代，以改善甘薯的明确和特定性状。