【摘要】 提出了一个基于链图将Bayesian网的结点聚集算法。将Bayesian网转化为链图,将链图上等价的结点集当作一个领域并用一个新的结点来表示,修改整体结构和参数,从而完成对整个Bayesian网的修正。聚集之后的Bayesian网可以使领域之间的概率关系更清晰明显,优化Bayesian网的结构表示。更多还原

【Abstract】 Based on chain graph, we present an algorithm to aggregate the nodes in a Bayesian network. Firstly, Bayesian network was transformed into chain graph. The relation between nodes in chain graphs partitioned nodes into some subset of nodes viewed as domains. Then, each domain was aggregated into a new node in the new Bayesian network. Finally, By refining the parameters, we constructed the Bayesian network again. This procedure make the probabilistic relation between domains more clear, and at the same time, make the structure of the Bayesian network can be optimized.更多还原