论文标题
迭代比例缩放下的理性分区模型
Rational partition models under iterative proportional scaling
论文作者
论文摘要
在这项工作中，我们研究了分区模型，即对数线性模型的子集，该模型可以执行迭代比例缩放（IPS）算法以数值计算最大似然估计（MLE）。分区模型包括层次模型和平衡，分层的树木等模型的家族。我们在分区模型的矩阵表示上定义了一个足够的条件，称为广义运行相交属性（GRIP），IPS算法在一个周期内产生精确的MLE。此外，我们将握把连接到感谢您的福利纤维产品，并与层次模型和平衡，分层的树木的先前结果联系起来。这导致了平衡，分层分阶段的树木的特征。
In this work we investigate partition models, the subset of log-linear models for which one can perform the iterative proportional scaling (IPS) algorithm to numerically compute the maximum likelihood estimate (MLE). Partition models include families of models such as hierarchical models and balanced, stratified staged trees. We define a sufficient condition, called the Generalized Running Intersection Property (GRIP), on the matrix representation of a partition model under which IPS algorithm produces the exact MLE in one cycle. Additionally we connect the GRIP to the toric fiber product and to previous results for hierarchical models and balanced, stratified staged trees. This leads to a characterization of balanced, stratified staged trees in terms of the GRIP.
