Data-Based Construction of Multidimensional Probabilistic Models with MUDIM
Data-Based Construction of Multidimensional Probabilistic Models with MUDIM
复制标题
使用 MUDIM 基于数据构建多维概率模型
DOI：
10.1093/jigpal/jzl021
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
R. Jirousek
中科院分区：
文献类型：
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作者：
R. Jirousek
本文的目的是介绍一个程序系统MUDIM，并展示它如何用于多维概率模型的构造。该系统正在开发中，目标是获得一种工具，用于使用成分模型进行实验计算，在某种程度上，这种模型是贝叶斯网络的替代方案。这些模型是基于这样一种想法，即从大量低维分布组成多维分布。在考虑基于知识的系统时，这种方法很自然地解决了表达关于实践领域的全球知识的困难。我们只需与当地知识的系统合作，而全球知识必须从该系统中汇集而来。
The goal of the paper is to introduce a program system, MUDIM, and to show how it can be used for multidimensional probabilistic model construction. The system is being developed with the goal to gain a tool for experimental computations with compositional models which are, in a way, an alternative to Bayesian networks. These models are based on the idea of composing a multidimensional distribution from a great number of low-dimensional ones. When considering knowledge-based systems, this approach quite naturally cope with the difficulty of expressing global knowledge about a field of practise. We have only to work with a system of pieces of local knowledge from which the global knowledge must be assembled.