Compatible prior distributions for directed acyclic graph models
成果类型：
Article
署名作者：
Roverato, A; Consonni, G
署名单位：
Universita di Modena e Reggio Emilia; University of Pavia
刊物名称：
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
ISSN/ISSBN：
1369-7412
DOI：
10.1111/j.1467-9868.2004.00431.x
发表日期：
2004
页码：
47-61
关键词：
摘要：
The application of certain Bayesian techniques, such as the Bayes factor and model averaging, requires the specification of prior distributions on the parameters of alternative models. We propose a new method for constructing compatible priors on the parameters of models nested in a given directed acyclic graph model, using a conditioning approach. We define a class of parameterizations that is consistent with the modular structure of the directed acyclic graph and derive a procedure, that is invariant within this class, which we name reference conditioning.
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