Bayesian clustering in decomposable graphs
Bayesian clustering in decomposable graphs
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DOI：
10.1214/11-ba630
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发表时间：
2011-01-01
影响因子：
4.4
通讯作者：
Caron, Francois
中科院分区：
文献类型：
--
作者：
Bornn, Luke;Caron, Francois
在本文中，我们提出了一类可分解图的先验分布，以提高建模灵活性。虽然现有的方法仅惩罚边的数量，但所提出的工作使从业者能够控制聚类、分离级别和图的其他特征。重点放在特定的先验分布上，该先验分布的动机来自产品划分模型类；通过理论和模拟来检查该先验相对于现有先验的属性。然后，我们演示了图形模型在农业领域的使用，展示了所提出的先验分布如何缓解先前方法在正确建模不同作物品种产量之间的相互作用方面的不灵活性。最后，我们探讨了美国的投票数据，比较了上个世纪各州的投票模式。
In this paper we propose a class of prior distributions on decomposable graphs, allowing for improved modeling flexibility. While existing methods solely penalize the number of edges, the proposed work empowers practitioners to control clustering, level of separation, and other features of the graph. Emphasis is placed on a particular prior distribution which derives its motivation from the class of product partition models; the properties of this prior relative to existing priors are examined through theory and simulation. We then demonstrate the use of graphical models in the field of agriculture, showing how the proposed prior distribution alleviates the inflexibility of previous approaches in properly modeling the interactions between the yield of different crop varieties. Lastly, we explore American voting data, comparing the voting patterns amongst the states over the last century.