Random Generation of Bayesian Networks
Random Generation of Bayesian Networks
复制标题
贝叶斯网络的随机生成
DOI：
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发表时间：
2002
期刊：
影响因子：
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通讯作者：
Fabio Gagliardi Cozman
中科院分区：
文献类型：
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作者：
J. Ide;Fabio Gagliardi Cozman
提出了一种新的随机贝叶斯网络生成方法。这种方法可以用来测试贝叶斯网络的推理和学习算法，并获得对这种网络的平均属性的见解。任何生成贝叶斯网络的方法都必须首先生成有向无环图（网络的“结构”），然后为生成的图生成条件概率分布。目前文献中没有算法提供关于生成的贝叶斯网络的分布的保证。使用工具的马尔可夫链理论，我们提出了算法，可以产生均匀分布的样本的有向无环图。我们介绍的方法，统一生成的多连接和单连接网络的给定数量的节点，节点度和弧数的约束可以很容易地施加。在均匀生成有向无圈图后，通过对Dirichlet分布进行采样，得到条件分布。
This paper presents new methods for generation of random Bayesian networks. Such methods can be used to test inference and learning algorithms for Bayesian networks, and to obtain insights on average properties of such networks. Any method that generates Bayesian networks must first generate directed acyclic graphs (the "structure" of the network) and then, for the generated graph, conditional probability distributions. No algorithm in the literature currently offers guarantees concerning the distribution of generated Bayesian networks. Using tools from the theory of Markov chains, we propose algorithms that can generate uniformly distributed samples of directed acyclic graphs. We introduce methods for the uniform generation of multi-connected and singly-connected networks for a given number of nodes; constraints on node degree and number of arcs can be easily imposed. After a directed acyclic graphis uniformly generated, the conditional distributions are produced by sampling Dirichlet distributions.