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摘要翻译：
对于一个给定的离散可分解图形模型，我们识别了几个可供选择的参数，并构造了相应的参考先验值以获得合适的参数分组。具体地说，假设图的团按完全顺序排列，我们考虑的参数是给定分隔符的团残差的条件概率，以及广义对数比。我们还考虑了与表示统计模型切分的变量集合相关联的参数化。我们得到的参考先验不依赖于群的顺序，属于共轭族，是正确的。
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英文标题：
《Alternative parametrizations and reference priors for decomposable
discrete graphical models》
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作者：
Guido Consonni, H\'el\`ene Massam
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最新提交年份：
2007
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分类信息：
一级分类：Mathematics 数学
二级分类：Statistics Theory 统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics 统计学
二级分类：Statistics Theory 统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
For a given discrete decomposable graphical model, we identify several alternative parametrizations, and construct the corresponding reference priors for suitable groupings of the parameters. Specifically, assuming that the cliques of the graph are arranged in a perfect order, the parameters we consider are conditional probabilities of clique-residuals given separators, as well as generalized log-odds-ratios. We also consider a parametrization associated to a collection of variables representing a cut for the statistical model. The reference priors we obtain do not depend on the order of the groupings, belong to a conjugate family, and are proper.
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PDF链接：
https://arxiv.org/pdf/707.3873
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