论文标题
概率依赖图
Probabilistic Dependency Graphs
论文作者
论文摘要
我们介绍了概率依赖图（PDG），这是一种新的有向图形模型。 PDG可以以自然的方式捕获不一致的信念，并且比贝叶斯网络（BNS）更模块化，因为它们使整合新信息并重组表示形式变得更加容易。我们以示例说明PDG是一种特别自然的建模工具。我们为PDG提供三种语义，每个语义都可以从评分函数（在网络中的变量上的联合分布上）得出，这些函数可以看作是代表分布与PDG的不相容性。对于与BN相对应的PDG，该函数被BN所代表的分布最小化，表明PDG语义扩展了BN语义。我们进一步表明，因素图及其指数族也可以忠实地表示为PDG，而使用因子图对PDG进行建模有重大障碍。
We introduce Probabilistic Dependency Graphs (PDGs), a new class of directed graphical models. PDGs can capture inconsistent beliefs in a natural way and are more modular than Bayesian Networks (BNs), in that they make it easier to incorporate new information and restructure the representation. We show by example how PDGs are an especially natural modeling tool. We provide three semantics for PDGs, each of which can be derived from a scoring function (on joint distributions over the variables in the network) that can be viewed as representing a distribution's incompatibility with the PDG. For the PDG corresponding to a BN, this function is uniquely minimized by the distribution the BN represents, showing that PDG semantics extend BN semantics. We show further that factor graphs and their exponential families can also be faithfully represented as PDGs, while there are significant barriers to modeling a PDG with a factor graph.
