Learning Undirected Possibilistic Networks with Conditional Independence Tests

C. Borgelt
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Abstract

Approaches based on conditional independence tests are among the most popular methods for learning graphical models from data. Due to the predominance of Bayesian networks in the field, they are usually developed for directed graphs. For possibilistic networks of a certain kind, however, undirected graphs are a more natural basis and thus algorithms for learning undirected graphs are desirable in this area. In this paper I present an algorithm for learning undirected graphical models, which is derived from the well-known Cheng-Bell-Liu algorithm. Its main advantage is the lower number of conditional independence tests that are needed, while it achieves results of comparable quality.
用条件独立性检验学习无向可能性网络
基于条件独立测试的方法是从数据中学习图形模型的最流行的方法之一。由于贝叶斯网络在该领域的优势,它们通常用于有向图。然而,对于某种可能性网络,无向图是一个更自然的基础,因此学习无向图的算法在这个领域是可取的。本文提出了一种学习无向图模型的算法,该算法来源于著名的Cheng-Bell-Liu算法。它的主要优点是所需的条件独立性测试数量较少,同时可以获得相当质量的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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