Growth models are used to understand the relationships in production during the life of an animal, being an abstraction of their natural dynamics. In this context, the objective of this research was to fit a curve for weight of Santa Ines sheep using frequentist and Bayesian approaches, present strategies for eliciting prior distributions for the latter and compare the results obtained with each one. Growth data from a literature study was used as sample. The parameter estimates were obtained using nonlinear least squares in the frequentist approach and using Monte Carlo method via Markov Chains algorithms in the Bayesian approach. Noninformative and informative prior distributions were used in the Bayesian approach, with prior information coming from other six studies. A methodology for eliciting informative prior distributions was provided. Prior information contributed to more precise estimates of sheep weight. It was seen that predominance of prior information may produce inconsistent interval estimates. Although the values of the parameters estimated by the two approaches were similar, the use of the Bayesian approach, together with the prior distributions, allowed for good and more precise estimates when compared to the frequentist approach.

生长模型用于理解动物生命过程中生产中的关系，是其自然动力的抽象。在这种情况下，本研究的目的是使用频度法和贝叶斯方法拟合圣塔伊内斯绵羊的体重曲线，提出引起后者先验分布的策略，并比较每种方法的结果。来自文献研究的生长数据用作样本。参数估计是在频度方法中使用非线性最小二乘法，在贝叶斯方法中使用Markov Chains算法通过蒙特卡洛方法获得的。贝叶斯方法使用了非信息性和信息性先验分布，先验信息来自其他六项研究。提供了一种方法，用于得出信息丰富的先前分布。先前的信息有助于更精确地估计绵羊的体重。可以看出，先验信息的优势可能会产生不一致的区间估计。尽管两种方法估计的参数值相似，但与贝叶斯方法相比，使用贝叶斯方法以及先验分布可以进行更好且更准确的估计。