A glimpse of symbolic-statistical modeling by PRISM
A glimpse of symbolic-statistical modeling by PRISM
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DOI：
10.1007/s10844-008-0062-7
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发表时间：
2008-10
影响因子：
3.4
通讯作者：
Taisuke Sato
中科院分区：
文献类型：
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作者：
Taisuke Sato
我们给出了一个简单的概述，基于逻辑的符号建模语言PRISM，它提供了一个统一的方法，生成概率模型，包括贝叶斯网络，隐马尔可夫模型和概率上下文无关文法。我们包括一些实验结果与概率上下文无关文法提取的Penn树库。我们还展示了EM学习的概率上下文无关图语法作为探索一个新领域的一个例子。
We give a brief overview of a logic-based symbolic modeling language PRISM which provides a unified approach to generative probabilistic models including Bayesian networks, hidden Markov models and probabilistic context free grammars. We include some experimental result with a probabilistic context free grammar extracted from the Penn Treebank. We also show EM learning of a probabilistic context free graph grammar as an example of exploring a new area.