CABeN: A Collection of Algorithms for Belief Networks
CABeN: A Collection of Algorithms for Belief Networks
复制标题
CABeN：信念网络算法集合
DOI：
10.7936/k71g0jnh
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发表时间：
1991
影响因子：
2.4
通讯作者：
M. Frisse
中科院分区：
文献类型：
--
作者：
S. Cousins;William Chen;M. Frisse
信念网络已经成为一种越来越流行的处理系统中不确定性的机制。不幸的是，已知在给定一组证据的情况下找到置信网络节点的概率值通常是不容易处理的。已经提出并实现了许多不同的模拟算法来近似解决这个问题。在这份报告中，我们描述了这样的算法，CABeN的集合的实现。CABeN包含一个用于模拟信念网络的例程库，一个通过任何“tty”界面上的菜单访问例程的程序，以及一些演示如何在应用程序中使用该库的示例程序。CABeN实现了五种算法：逻辑抽样，Likestrant，加权（Shachter的基本算法），自我重要性，Pearl的算法和Chavez的算法。此外，我们还实现了马尔可夫评分作为上述任何算法的一个选项。我们在一系列实验中比较了这10种变化，在这些实验中，我们改变了图的拓扑结构、提供证据的节点数量和条件概率值。详细描述每一个…阅读第2页的完整摘要。
Belief networks have become an increasingly popular mechanism for dealing with uncertainty in systems. Unfortunately, it is known that finding the probability values of belief network nodes given a set of evidence is not tractable in general. Many different simulation algorithms for approximating solutions to this problem have been proposed and implemented. In this report, we describe the implementation of a collection of such algorithms, CABeN. CABeN contains a library of routines for simulating belief networks, a program for accessing the routines through menus on any 'tty' interface, and some sample programs demonstrating how the library would be used within an application. CABeN implements five algorithms: Logic Sampling, Likelihood, Weighting (Shachter's Basic algorithm), Self Importance, Pearl's algorithm, and Chavez's algorithm. In addition, we have implemented Markov scoring as an option to any of the above algorithms. We have compared these 10 variations with each other in a series of experiments in which we varied the graph topologies, the number of nodes provided with evidence, and the conditional probability values. A detailed description of each... Read complete abstract on page 2.