Complex Probabilistic Modeling with Recursive Relational Bayesian Networks

被引：0
|
作者：
Manfred Jaeger
机构：
[1] Max-Planck-Institut für Informatik,
来源：
Annals of Mathematics and Artificial Intelligence | 2001年 / 32卷
关键词：
first-order probabilistic representations; knowledge based model construction; Bayesian networks; temporal and relational models;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A number of representation systems have been proposed that extend the purely propositional Bayesian network paradigm with representation tools for some types of first-order probabilistic dependencies. Examples of such systems are dynamic Bayesian networks and systems for knowledge based model construction. We can identify the representation of probabilistic relational models as a common well-defined semantic core of such systems.
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页码：179 / 220
页数：41
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