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Application of Bayesian Network Learning Methods to Waste Water Treatment Plants


Application of bayesian network learning methods to land resource evaluation


The same training and testing datasets were also used for Bayesian network learning.


The dependency analysis by Bayesian network learning is described in Section 3.


The effect of noise in the data on Bayesian network learning of the urea cycle.

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 Bayesian network is becoming more remarkable in AI research fields,which plays important role in modern expert system,diagnoses system and decision system.Bayesian network works on three points as below:knowledge representation,learning and inference.Prababilistic methods are its mathematical fundamental which helps learning distribution from data and leads Bayesian theory to real application.This paper introduces various common methods in probability data learning... Bayesian network is becoming more remarkable in AI research fields,which plays important role in modern expert system,diagnoses system and decision system.Bayesian network works on three points as below:knowledge representation,learning and inference.Prababilistic methods are its mathematical fundamental which helps learning distribution from data and leads Bayesian theory to real application.This paper introduces various common methods in probability data learning and make comparison among them under various application background.The methods based on classical statistics have a matured theory and a set of simple and direct calculation.But they reply heavily on sample data,which apply only those information from sample data with expert knowledge left aside.Bayesian Network combines information of expert knowledge and sample data together.It can give more accurate learning result and rely less on sample data.Parameter learning is main part of learning Bayesian Network models,and it's the basis of Bayesian Network learning.  Bayesian网已经成为AI领域的研究热点 ,并在现代专家系统、诊断系统及决策支持系统中发挥着至关重要的作用 .Bayesian网的研究主要集中在三个方面 :知识表示、学习与推理 .概率知识是Bayesian网坚实的数学基础 ,从数据中学习分布参数使得Bayesian网逐步走向现实应用 .本文介绍和比较了概率参数学习的各种常用方法 ,并探求了它们在不同应用背景下的优缺点 .基于经典统计学的方法理论成熟 ,计算简单 ,但它只利用了实例数据集合所提供的信息 ,无法加入专家知识 ,对实例数据的依赖性大 ;基于Bayesian有机结合了两类信息 ,对实例数据的依赖性降低 ,学习结果更加准确 .参数学习是Bayesian网学习的基础 ,是Bayesian网结构学习必不可少的部分 .  For the given threshold, a new method of constructing the Bayesian network is proposed by computing the mutual information between two variables. The traditional Markov Chain Monte Carlo method for structural learning in graphical models MCMC algorithm is improved. Based on the improved algorithm, Markov Chain of the Bayesian network is got. The result of the experiment show that the Bayesian network learned by the improved method is similar to that learned by the... For the given threshold, a new method of constructing the Bayesian network is proposed by computing the mutual information between two variables. The traditional Markov Chain Monte Carlo method for structural learning in graphical models MCMC algorithm is improved. Based on the improved algorithm, Markov Chain of the Bayesian network is got. The result of the experiment show that the Bayesian network learned by the improved method is similar to that learned by the old algorithm, and their accepted ratio is also very similar.  对于给定的阈值,通过计算变量之间的互信息,设计了一种构造贝叶斯网络结构的方法。改进了关于图模结构学习中常见的 MCMC 算法。将这种方法构造的贝叶斯网络作为马尔可夫链初始状态的网络结构,利用改进后的 MCMC 算法,构造一个关于贝叶斯网络结构的马尔可夫链。迭代给定次数后,得到关于变量组的贝叶斯网络结构。实验结果表明:改进前和改进后的两种方法得到的贝叶斯网络结构基本一致,网络结构的接受率也相近。  An incremental approach to learning Bayesian networks based on genetic algorithm,namely ILBN,is put forward in this paper.ILBN introduces the EM algorithm and genetic algorithm into the incremental process of Bayesian network learning,calculates the expectation of the sufficient statistics with incomplete data using EM algorithm and evolves network structures using genetic algorithm,that could avoid getting into local maxima to some extent.Furthermore,by defining a new mutation... An incremental approach to learning Bayesian networks based on genetic algorithm,namely ILBN,is put forward in this paper.ILBN introduces the EM algorithm and genetic algorithm into the incremental process of Bayesian network learning,calculates the expectation of the sufficient statistics with incomplete data using EM algorithm and evolves network structures using genetic algorithm,that could avoid getting into local maxima to some extent.Furthermore,by defining a new mutation operator and extending the traditional crossover operator,ILBN could incrementally learn and evolve Bayesian networks containing hidden variables.Finally,ILBN improves the incremental process by Friedman et al.The experimental results show that,in terms of storage cost,ILBN is comparable with the method by Friedman et al,while under the same experimental conditions,ILBN could learn more accurate networks than that of Friedman et al.The experimental results also verify the validity of ILBN in presence of incomplete data and hidden variables.  提出了一种贝叶斯网络增量学习方法———ILBN.ILBN将EM算法和遗传算法引入到了贝叶斯网络的增量学习过程中,用EM算法从不完整数据计算充分统计量的期望,用遗传算法进化贝叶斯网络的结构,在一定程度上缓解了确定性搜索算法的局部极值问题.通过定义新变异算子和扩展传统的交叉算子,ILBN能够增量学习包含隐变量的贝叶斯网络结构.最后,ILBN改进了Friedman等人的增量学习过程.实验结果表明,ILBN和Friedman等人的增量学习方法存储开销相当,但在相同条件下,学到的网络更精确;实验结果也证实了存在不完整数据和隐变量时,ILBN的增量学习能力.   << 更多相关文摘 
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