用贝叶斯网络进行因果分析

因果分析是贝叶斯网络的一个重要应用领域。因果分析不同于相关分析,无论对数据分析、扰动分析还是预测都是十分重要的。贝叶斯网络虽然有一定的因果语义(我们常常用变量的因果关系构造贝叶斯网络结构),但贝叶斯网络是条件独立性的表示,因此我们不能不加限定地用贝叶斯网络进行因果分析。贝叶斯     

高效计算因果网中的最大可能解释

在因果网中,高效计算的最大可能解释(Most Probable Explanations,MPE)是一个关键问题。从有向无环图的角度,研究者们发现每一个因果网都有一个与之对应的贝叶斯网络。文中通过比较干预和微分的语义,揭示了MPE完全原子干预的微分语义。根据微分语义,因果网中原子干预MPE实例的计算可以归约为贝叶斯网络中的MPE实例的计算。接着,提出了一个联合树算法(Best JoinTree,BJT),它通过在因果网中只构建一个联合树来计算最好的原子干预,原子干预的结果包含一个BMPE(Best MPE)概率和它对应的实例。其中,BMPE概率是对MPE所有结点分别进行原子干预后得到的最高概率。BJT可以采用干预的效果来计算对应贝叶斯网络的MPE概率和MPE实例。最后,实验证实了绝大多数因果网在计算最好原子干预时,BJT的速度比目前最好的算法快了超过10倍。     

高效计算因果网中的干预

在因果网中,对和积问题因果效果的计算是其首要问题,从有向无环图的角度,研究者们发现每一个因果网都有一个与之对应的贝叶斯网络,干预是因果网的一个基本操作.类似于贝叶斯网络中的剪枝策略,在剪枝掉所有无效结点后,文中设计了一种优化的算法OFDo来计算对因果网中每个结点的完全原子干预.文中接着研究多干预操作,发现多干预操作具有...     

Linking Bayesian networks and PLS path modeling for causal analysis

Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed.     

Approximations of Bayesian networks through KL minimisation

Exact inference in large, complex Bayesian networks is computationally intractable. Approximate schemes are therefore of great importance for real world computation. In this paper we consider an approximation scheme in which the original Bayesian network is approximated by another Bayesian network. The approximating network is optimised by an iterative procedure, which minimises the Kullback-Leibler divergence between the two networks. The procedure is guaranteed to converge to a local minimum of the Kullback-Leibler divergence. An important question in this scheme is how to choose the structure of the approximating network. In this paper we show how redundant structures of the approximating model can be pruned in advance. Simulation results of model optimisation are provided to illustrate the methods. Wim Wiegerinck, Ph.D.: He has a postdoctoral position at Theoretical Foundation SNN, University of Nijmegen, the Netherlands. He obtained his M.S. in physics from the University of Amsterdam, the Netherlands in 1988 and Ph.D. degree in physics from the University of Nijmegen, the Netherlands in 1996. His current research interest is in theory and applications of graphical models and artificial neural networks. Bert Kappen, Ph.D.: He studied particle physics in Groningen, the Netherlands and completed his PhD in this field in 1987 at the Rockefeller University in New York. From 1987 to 1989 he worked as a scientist at the Philips Research Laboratories in Eindhoven, the Netherlands. Presently, he is associate professor of physics at SNN University of Nijmegen, conducting research in artificial and biological neural networks.     

基于领域知识的贝叶斯网络结构学习算法

针对SEM算法在缺省数据学习中存在精度偏低和收敛速度缓慢的问题,通过将领域知识引入到SEM算法中,提出了KB-SEM算法,该算法首先用D-S证据理论综合领域知识,然后将采集的知识以禁忌表的方式嵌入SEM中来限制和引导算法的搜索路径,缩小算法的搜索空间。实验表明,KB-SEM算法能有效地提高算法的学习精度和时间性能,且能在一定程度上避免主观偏见和数据噪音的干扰。     

用于态势评估中因果推理的贝叶斯网络

1 引言贝叶斯网络是由R.Howard和J.Matheson于1981年提出来的,它主要用来表述不确定的专家知识。后来经过J.Pearl,D.Heckerman等人的研究,贝叶斯网络的理论及算法有了很大的发展。作为一种知识表示和进行概率推理的框架,贝叶斯网络在具有内在不确定性的推理和决策问题中已经得到了广泛的应用,例如概率专家系统、计算机视觉和数据挖掘等。     

Restricted Bayesian classification networks

Bayesian networks are graphical models that describe dependency relationships between variables, and are powerful tools for studying probability classifiers. At present, the causal Bayesian network learning method is used in constructing Bayesian network classifiers while the contribution of attribute to class is over-looked. In this paper, a Bayesian network specifically for classification-restricted Bayesian classification networks is proposed. Combining dependency analysis between variables, classification accuracy evaluation criteria and a search algorithm, a learning method for restricted Bayesian classification networks is presented. Experiments and analysis are done using data sets from UCI machine learning repository. The results show that the restricted Bayesian classification network is more accurate than other well-known classifiers.     

贝叶斯网络精确推理算法的研究

贝叶斯网络是以概率理论为基础的不确定知识表示模型,贝叶斯网络推理的目的是得到随机变量的概率分布。目前,最流行的推理算法是联合树算法,它的主要思想是将贝叶斯网络转化为一棵无向树,在无向树上完成消息传递过程,求出原贝叶斯网络中任意随机变量的概率分布。为了降低算法的计算时空复杂度,对算法进行了不断的改进,为贝叶斯网络推理算法的进一步研究提供了条件。     

Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks

This paper presents a methodological approach based on Bayesian Networks for modelling the behaviour of the students of a bachelor course in computers in an Open University that deploys distance educational methods. It describes the structure of the model, its application for modelling the behaviour of student groups in the Informatics Course of the Hellenic Open University, as well as the advantages of the presented method under conditions of uncertainty. The application of this model resulted in promising results as regards both prediction of student behaviour, based on modelled past experience, and assessment (i.e., identification of the reasons that led students to a given ‘current' state). The method presented in this paper offers an effective way to model past experience, which can significantly aid in decision-making regarding the educational procedure. It can also be used for assessment purposes regarding a current state enabling tutors to identify mistakes or bad practices so as to avoid them in the future as well as identify successful practices that are worth repeating. The paper concludes that modelling is feasible and that the presented method is useful especially in cases of large amounts of data that are hard to draw conclusions from without any modelling. It is emphasised that the presented method does not make any predictions and assessments by itself; it is a valuable tool for modelling the educational experience of its user and exploiting the past data or data resulting from its use.     

贝叶斯网络在汽轮机故障诊断中的应用

针对在汽轮发电机组振动故障诊断中的不确定性问题，提出了应用贝叶斯网络对其进行推理和诊断。本文介绍了贝叶斯网络的建模方法与推理机制，并通过专家系统的建模过程与诊断实例，证明了应用贝叶斯网络对汽轮发电机组进行故障诊断所具有的独特优点。     

动态贝叶斯网络在战术态势估计中的应用*

针对战术态势估计的特点和要求,分析和建立了应用于态势估计的动态贝叶斯网络模型。该模型以离散变量集为研究对象。由于该动态贝叶斯网络满足Markovian特性和平稳特性,降低了网络的复杂度。相比较于贝叶斯网络模型,该动态贝叶斯网络模型考虑了时序因素,将前时刻的态势因素作为当前时刻态势估计的证据的一部分,并能对下一时刻的态势进行预测。文中采用集树（junction tree）算法,利用相关的贝叶斯网络推理软件进行了实验,实验结果表明基于动态贝叶斯网络的估计结果较贝叶斯网络的估计结果好,验证了该模型的有效性。     

基于互信息的直接因果搜索算法

因果关系挖掘是数据挖掘领域一个新的研究方向,具有很大的实用意义,但理论建模的困难阻碍了它的发展。20世纪90年代,在因果建模研究成果的基础上,国外开始针对此方向进行研究,目前已得到了一些理论算法。该文针对已有算法鲁棒性、实用性差,不适合大规模数据挖掘应用的缺点,提出了一种基于互信息的因果数据挖掘算法———直接因果搜索算法;仿真试验表明,该算法能很好地搜索出目标变量的直接因果,与其他算法相比,对于数据、门限的变化更具鲁棒性。     

Learner attending auto-monitor in distance learning using image recognition and Bayesian Networks

Distance learning is one of the common education methods. Its advantage lies in that the student can learn at anytime or anyplace. However, such a learning mode relies highly on the dependence of the student. Under different environments and conditions, not all the students can be attentive. In this research, an auto-detection system has been designed, using image processing and recognition technique, for defining the facial expressions and behavior easily found when a learner is inattentive or in bad mentality under distance learning environment. From the learner’s facial expressions and behavior features, the attentiveness of the student during distance learning can be determined by Bayesian Networks Model.After implementing the system of this research, and performing practical test, it is found that the accuracy of identifying the features of bad mentality and inattentive behavior is high. From Bayesian Networks assessment and inference, the learning attentiveness of the student can be determined precisely to have the teacher control the learning condition of the student explicitly.     

动态贝叶斯网络在水文预报中的应用

贝叶斯网络是目前人工智能中不确定知识与推理中最有效的理论模型之一。提出一种基于动态贝叶斯网络模型理论的水文预报方法。在综合考虑降雨径流成因的基础上,利用领域专家知识构建网络模型,在已有降雨、流量数据的基础上通过计算变量间的条件概率来计算流量发生的可能性。最后,通过渭河流域咸阳至临潼段历时数据进行仿真实验,对仿真结果和该模型进行了分析。     

Explaining inferences in Bayesian networks

While Bayesian network (BN) can achieve accurate predictions even with erroneous or incomplete evidence, explaining the inferences remains a challenge. Existing approaches fall short because they do not exploit variable interactions and cannot account for compensations during inferences. This paper proposes the Explaining BN Inferences (EBI) procedure for explaining how variables interact to reach conclusions. EBI explains the value of a target node in terms of the influential nodes in the target’s Markov blanket under specific contexts, where the Markov nodes include the target’s parents, children, and the children’s other parents. Working back from the target node, EBI shows the derivation of each intermediate variable, and finally explains how missing and erroneous evidence values are compensated. We validated EBI on a variety of problem domains, including mushroom classification, water purification and web page recommendation. The experiments show that EBI generates high quality, concise and comprehensible explanations for BN inferences, in particular the underlying compensation mechanism that enables BN to outperform alternative prediction systems, such as decision tree.     

Interactive structural learning of Bayesian networks

We propose an hybrid approach for structure learning of Bayesian networks, in which a computer system and a human expert cooperate to search for the best structure. The system builds an initial tree structure which is graphically presented to the expert, and then the expert can modify this structure according to his knowledge of the domain. The system has several tools for aiding the human in this task: it allows for graphical editing (adding, deleting, inverting arcs) of the network, it shows graphically the correlation between variables, and it gives a measure of the quality and complexity for each structure. A measure which combines both quality and complexity, that we call quality, is defined. We have tested the tool in two domains: atmospheric pollution and car insurance, with good results.     

Bayesian networks for imputation in classification problems

Missing values are an important problem in data mining. In order to tackle this problem in classification tasks, we propose two imputation methods based on Bayesian networks. These methods are evaluated in the context of both prediction and classification tasks. We compare the obtained results with those achieved by classical imputation methods (Expectation–Maximization, Data Augmentation, Decision Trees, and Mean/Mode). Our simulations were performed by means of four datasets (Congressional Voting Records, Mushroom, Wisconsin Breast Cancer and Adult), which are benchmarks for data mining methods. Missing values were simulated in these datasets by means of the elimination of some known values. Thus, it is possible to assess the prediction capability of an imputation method, comparing the original values with the imputed ones. In addition, we propose a methodology to estimate the bias inserted by imputation methods in classification tasks. In this sense, we use four classifiers (One Rule, Naïve Bayes, J4.8 Decision Tree and PART) to evaluate the employed imputation methods in classification scenarios. Computing times consumed to perform imputations are also reported. Simulation results in terms of prediction, classification, and computing times allow us performing several analyses, leading to interesting conclusions. Bayesian networks have shown to be competitive with classical imputation methods.     

贝叶斯网络扩展研究综述

贝叶斯网络是一种能够对复杂不确定系统进行推理和建模的有效工具,广泛用于不确定决策、数据分析以及智能推理等领域.由于理论和实际的需要,贝叶斯网络不断扩展,出现了各种模型和研究方法.为此,综述了贝叶斯网络在不同领域的扩展模型以及在不同理论框架下的进展,并展望了未来的几个发展方向.     

Detection of Unfaithfulness and Robust Causal Inference

Much of the recent work on the epistemology of causation has centered on two assumptions, known as the Causal Markov Condition and the Causal Faithfulness Condition. Philosophical discussions of the latter condition have exhibited situations in which it is likely to fail. This paper studies the Causal Faithfulness Condition as a conjunction of weaker conditions. We show that some of the weaker conjuncts can be empirically tested, and hence do not have to be assumed a priori. Our results lead to two methodologically significant observations: (1) some common types of counterexamples to the Faithfulness condition constitute objections only to the empirically testable part of the condition; and (2) some common defenses of the Faithfulness condition do not provide justification or evidence for the testable parts of the condition. It is thus worthwhile to study the possibility of reliable causal inference under weaker Faithfulness conditions. As it turns out, the modification needed to make standard procedures work under a weaker version of the Faithfulness condition also has the practical effect of making them more robust when the standard Faithfulness condition actually holds. This, we argue, is related to the possibility of controlling error probabilities with finite sample size (“uniform consistency”) in causal inference.