CONDITIONALLY EXTERNALLY BAYESIAN POOLING OPERATORS IN CHAIN GRAPHS
CONDITIONALLY EXTERNALLY BAYESIAN POOLING OPERATORS IN CHAIN GRAPHS
复制标题
链图中的条件外部贝叶斯池算子
DOI:
10.1214/aos/1031594740
复制
发表时间:
1997
影响因子:
4.5
通讯作者:
Jim Q. Smith
中科院分区:
文献类型:
--
作者:
Á. E. Faria;Jim Q. Smith
We address the multivariate version of French's group decision problem where the m members of a group, who are jointly responsible for the decisions they should make, wish to combine their beliefs about the possible values of n random variables into the group consensus probability distribution. We shall assume the group has agreed on the structure of associations of variables in a problem, as might be represented by a commonly agreed partially complete chain graph (PCG) we define in the paper. However, the members diverge about the actual conditional probability distributions for the variables in the common PCG. The combination algorithm we suggest they adopt is one which demands, at least on learning information which is common to the members and which preserves the originally agreed PCG structure, that the pools of conditional distributions associated with the PCG are externally Bayesian (EB). We propose a characterization for such conditionally EB (CEB) poolings which is more general and flexible than the characterization proposed by Genest, McConway and Schervish. In particular, such a generalization allows the weights attributed to the joint probability assessments of different individuals in the pool to differ across the distinct components of each joint density. We show that the group's commitment to being CEB on chain elements can be accomplished by the group being EB on the whole PCG when the group also agrees to perform the conditional poolings in an ordering compatible with evidence propagation in the graph.