Growth curves are commonly used in modeling aimed at crop yield prediction. Fitting such curves often depends on availability of detailed observations, such as individual grape bunch weight or individual apple weight. However, in practice, aggregated weights (such as a bucket of grape bunches or apples) are available instead. While treating such bucket averages as if they were individual observations is tempting, it may introduce bias particularly with respect to population variance. In this paper we provide an elegant solution which enables estimation of individual weights using Dirichlet priors within Bayesian inferential framework.

翻译：生长曲线常用于作物产量预测的建模中。拟合此类曲线通常依赖于详细观测数据的可获得性，例如单个葡萄串重量或单个苹果重量。然而实际中，往往只能获得聚合重量数据（如一桶葡萄串或苹果）。虽然将这类桶平均值视为个体观测值看似可行，但这可能会引入偏差，尤其在群体变异方面。本文提出了一种优雅的解决方案，通过在贝叶斯推断框架内使用狄利克雷先验来估计个体重量。