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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 .  Two new models for predicting the splice junction in eukaryotic DNA sequences were developed by exploiting Bayesian network, one for donor site and the other for acceptor site. The topology structures and the upstream (downstream) nodes of these two models were optimized in consideration of the biological characters of acceptor site and donor site. Both of the models were trained by a ML (maximum likelihood) algorithm for Bayesian network learning, then the testing DNA sequence data were feed... Two new models for predicting the splice junction in eukaryotic DNA sequences were developed by exploiting Bayesian network, one for donor site and the other for acceptor site. The topology structures and the upstream (downstream) nodes of these two models were optimized in consideration of the biological characters of acceptor site and donor site. Both of the models were trained by a ML (maximum likelihood) algorithm for Bayesian network learning, then the testing DNA sequence data were feed into the model and a 10fold cross validation method was used to evaluate the performance of prediction. The experimental results show that in average, the sensitivity of acceptor site detection was 92.5% and the specificity was 94.0%, the sensitivity of donor site detection was 92.3% and the specificity was 93.5%. These results proved that the models were better than the models based on independent matrix and conditional probability matrix, as well as the hidden Markov model for splice junction site detection in some ways. These conclusions indicate that the optimized Bayesian network models are powerful tools for splice junction detection in eukaryotic genes.  采用基于贝叶斯网络的建模方法,预测真核生物DNA序列中的剪接位点。分别建立了供体住点和受体位点模型,并根据两种位点的生物学特性,对模型的拓扑结构和上下游节点的选择进行了优化。通过贝叶斯网络的最大似然学习算法求出模型参数后,利用10分组交互验证方法对测试数据进行剪接位点预测。结果显示,受体位点的平均预测准确率为92.5％,伪受体位点的平均预测准确率为94.0％,供体位点的平均预测准确率为92.3％,伪供体位点的平均预测准确率为93.5％,整体效果要好于基于使用独立和条件概率矩阵、以及隐Markov模型的预测方法。表明利用贝叶斯网络对剪接位点建模是预测剪接位点的一种有效手段。  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。   << 更多相关文摘 
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