Bayesian Clustering of Farm Types Using the Mixtures Model
提出一种贝叶斯方法对农场进行分类，依据收入水平和波动性，发现新分类比传统方法更能体现农场内部技术特征的多样性，且组间收入差距更大、组内收入波动更小。
Abstract A Bayesian method of classifying observations that are assumed to come from a number of distinct subpopulations is outlined. The method is illustrated with simulated data and applied to the classification of farms according to their level and variability of income. The resultant classification shows a greater diversity of technical charactersitics within farm types than is conventionally the case. The range of mean farm income between groups in the new classification is wider than that of the conventional method and the variability of income within groups is narrower. Results show that the highest income group in 2000 included large specialist dairy farmers and pig and poultry producers, whilst in 2001 it included large and small specialist dairy farms and large mixed dairy and arable farms. In both years the lowest income group is dominated by non‐milk producing livestock farms.