有向无环图
图形模型
概率逻辑
贝叶斯网络
计算机科学
简单(哲学)
人工智能
理论计算机科学
统计模型
机器学习
任务(项目管理)
独立性(概率论)
条件独立性
数学
算法
统计
认识论
哲学
经济
管理
作者
Inteligencia Artiflcial,Juan del Rosal,Marek J. Druzdzel
摘要
The hardest task in knowledge engineering for probabilistic graphical models, such as Bayesian networks and in∞uence diagrams, is obtaining their numerical parameters. Models based on acyclic directed graphs and composed of discrete variables, currently most common in practice, require for every variable a number of parameters that is exponential in the number of its parents in the graph, which makes elicitation from experts or learning from databases a daunting task. In this paper, we review the so called canonical models, whose main advantage is that they require much fewer parameters. We propose a general framework for them, based on three categories: deterministic models, ICI models, and simple canonical models. ICI models rely on the concept of independence of causal in∞uence and can be subdivided into noisy and leaky. We then analyze the most common families of canonical models (the OR/MAX, the AND/MIN, and the noisy XOR), generalizing them and ofiering criteria for applying them in practice. We also brie∞y review temporal canonical models.
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