由Markov网到Bayesian网

Markov网（马尔可夫网）是类似于Bayesian网（贝叶斯网）的另一种进行不确定性揄的有力工具，Markov网是一个无向图，而Bayesian网是一个有向无环图，发现Markov网不需要发现边的方向，因此要比发现Bayesian网容易得多，提出了一种通过发现Markov网得到等价的Bayesian网的方法，首先利用信息论中验证信息独立的一个重要结论，提出了一个基于依赖分析的边删除算法发现Markov网，该算法需O（n^2)次CI（条件独立）测试，CI测试的时间复杂度取决于由样本数据得到的联合概率函数表的大小，经证明，假如由样本数据得到的联合概率函数严格为正，则该算法发现的Markov网一定是样本的最小L图，由发现Markov网，根据表示的联合概率函数相等，得到与其等价的Bayesian网。     

基于扩展关系模型的多Bayesian网依赖结构的合并

贝叶斯网是一个每个结点都带有一张概率表的有向无环图，它可以有效地表示不确定性知识并进行知识推理。知识系统在很多时候不得不将来自不同信息源或者同一信息源不同时刻的知识合并起来。Bayesian网作为一个知识系统，所以也会面临将多个Bayesian网提供的信息结合起来。本文提出一个基于扩展的关系数据模型和条件独立的算法，该算法将多个Bayesian网合并成为一个Bayesian网，并且尽可能地保留每一个Bayesian网的信息。     

基于隐含变量信度网模型的脱机签名鉴别方法

本文提出一种新的基于信度网表示的签名鉴别方法。这种信度网表示方法和传统的信度网表示不同，其中的结点被划分成两类：共有结点和可选结点，以保证构造的网为树结构。该方法不仅可以获得每个结点的条件概率，而且可以表达与结点相关联的成分间的拓扑关系，以便对结构描述的不确定性和成分间的依赖关系进行处理。     

支持短时交通流量预测的概率图模型构建与推理

短时交通流量预测,是交通系统信息化和智能化交通运输管理技术领域研究的关键问题.目前的方法对历史数据具有较高的依赖程度,或者具有较高的计算成本,或者不能有效反映实际中较复杂的交通网络及各结点之间的相互关系、以及依赖的不确定性,或者多种模型的组合使得预测方法较复杂.贝叶斯网是一种重要的概率图模型,本文以交通网络结构为基础,利用概率图模型在不确定性知识表示和推理方面的良好性质,考虑路口交通流量及其预测的时序依赖特征,构建了带有时序条件依赖关系的交通贝叶斯网.进而针对短时交通流量预测的实时性和高效性要求,提出了基于Gibbs采样的交通贝叶斯网近似概率推理算法,并进行交通流量的短时预测.实验结果表明,本文提出的交通贝叶斯网构建、近似推理以及相应的短时交通流量的预测方法,具有高效性、准确性和可用性.     

Bayesian网的独立性推广模型

要本文提出了Bayesian同的独立性推广模型。Bayesian同能够表示变量之间概率影响关系与条件独立性，但不能表示因果独立性。虽然Noisy OR模型能够较好地表示变量之问的因果独立性，但该模型又因只能表示因果独立性而具有很大的局限性。本文提出的独立性推广模型解决了Bayesian同因果独立性表示能力不足的问题，扩展了Bayesian同与Noisy OR模型的表示范围，同时简化了Bayesian同的条件概率表，并且新模型更能够反映变量之间的概率影响关系。实验结果表明了该模型的实用性。     

Petri网与CDFG相结合的调度模型及其调度技术的研究

论文提出一种Petri网与控制数据流图(CDFG)结构相结合的调度模型,以及基于该模型的调度技术。Petri网表示VHDL源描述中的I/O时序信息,Petri网同一个位置结点中的信号赋值都是在同一个时刻进行的。根据每个位置结点中的语句再生成各自的CDFG。CDFG中条件的计算显式地表示在数据流部分,便于操作的调度。该CDFG结构上前驱操作和后继操作的判断不但与数据依赖相关,还与操作的条件相关。调度过程中既要考虑直接的数据依赖关系,还要考虑与条件相关的间接依赖关系。传统的调度算法需要经过修改才能应用到该模型上。     

基于概率SLCA的XML过滤

不确定数据管理逐渐成为一个重要的研究方向.作为网络交换重要标准的XML数据的不确定管理也成为一个研究热点.基于关键字的概率XML检索是其中一个重要的分支.目前对于概率XML关键字检索的研究,都只考察了结点之间的独立(IND)关系和互斥(MUX)关系.由于更普遍的结点依赖关系在表述和计算上的复杂性,较少有工作讨论.文中讨论概率XML模型PrXML~({exp,ind,mux})中基于SLCA语义的关键字过滤.这种模型中通过EXP结点描述更普遍的结点依赖关系.文中在定义了子树中关键字概率分布表tab及其相关的运算后,分别给出了模型中不同类型结点关键字概率分布表的计算方法,并给出了不需要构造可能世界直接求解SLCA结点概率的算法.文章通过实验评估了算法的特性和性能.     

基于Bayesian网络理论的远程查询系统

对Bayesian网及其概率推理进行了简述，提出并实现了一种以Bayesian理论为基础的查询方法，重点介绍Bayesian网络简化、扩展关系表、功能及概率推理算法。举例说明了系统运行的过程。     

基于拓扑信息加速马尔科夫毯学习

目标变量的马尔科夫毯（MB）是用于预测其状态的最优特征子集。提出一种新的约束学习类MB推导算法FSMB,它遵循后向选择的搜索策略,并依赖条件独立（CI）测试删除任意结点对之间的伪连接。与传统约束学习类算法不同,FSMB能从已执行的CI测试推导出不同结点扮演d 分割（d separation）结点的优先等级;而后基于该信息在未来优先执行条件集中包含高优先级结点的CI测试,从而更快速地判断并删除伪连接边。该策略可帮助快速缩小搜索空间,从而大大提升学习效率。基于仿真网络的实验研究显示,FSMB在计算效率上较经典的PCMB和IPC MB有显著的提升,而学习效果相当;在面对较大网络结构时（比如100和200个结点）,甚至比公认最快速的IAMB还节省近40%的计算量,但学习效果要远优于IAMB。基于16个UCI数据集和4个经典的分类模型的实验显示,基于FSMB输出的特征集合所训练模型的分类准确率普遍接近或高于基于原有特征全集训练所得模型。因此,FSMB是快速且有效的MB推导算法。     

数据挖掘技术在绿化决策中的应用

本文介绍了数据库技术的现状、数据挖掘的方法以及它在Bayesian网建网技术中的应用：通过数据挖掘解决Bayesian网络建模过程中所遇到的具体问题，即如何从大规模数据库中寻找各变量之间的关系以及如何确定条件概率问题。通过将该方法应用于实际问题中的例子：绿化决策系统中如何选取树种，我们将看到此技术是有效和实用的。     

Remote sensing data change detection based on the CI test of Bayesian networks

In recent years, the Bayesian network approach has been used as a data-mining tool in many information fields, but it has rarely been used to process remote sensing data. In this paper, we introduce a Bayesian network classifier for remote sensing data change detection. Using the conditional independence (CI) test we can find out relationships among the attributes and construct a Bayesian network that incorporates these relationship constraints.After geometric correction and radiometric normalization, a Bayesian network change detection system based on CI test algorithm is developed and applied to two temporal Landsat TM data acquired in 1994 and 2003 of Beijing area, and the overall change detection classification accuracy can get 92%. The experimental results show that Bayesian network is a newly effective approach for remote sensing data change detection.     

Causal independence for probability assessment and inference usingBayesian networks

A Bayesian network is a probabilistic representation for uncertain relationships, which has proven to be useful for modeling real-world problems. When there are many potential causes of a given effect, however, both probability assessment and inference using a Bayesian network can be difficult. In this paper, we describe causal independence, a collection of conditional independence assertions and functional relationships that are often appropriate to apply to the representation of the uncertain interactions between causes and effect. We show how the use of causal independence in a Bayesian network can greatly simplify probability assessment as well as probabilistic inference     

Learning Causal Bayesian Networks Using Minimum Free Energy Principle

Constraint-based search methods, which are a major approach to learning Bayesian networks, are expected to be effective in causal discovery tasks. However, such methods often suffer from impracticality of classical hypothesis testing for conditional independence when the sample size is insufficiently large. We present a new conditional independence (CI) testing method that is designed to be effective for small samples. Our method uses the minimum free energy principle, which originates from thermodynamics, with the “Data Temperature” assumption recently proposed by us. This CI method incorporates the maximum entropy principle and converges to classical hypothesis tests in asymptotic regions. In our experiments using repository datasets (Alarm/Insurance/Hailfinder/Barley/Mildew), the results show that our method improves the learning performance of the well known PC algorithm in the view of edge-reversed errors in addition to extra/missing errors.     

贝叶斯网模型的学习、推理和应用

近年来在人工智能领域,不确定性问题一直成为人们关注和研究的焦点。贝叶斯网是用来表示不确定变量集合联合概率分布的图形模式,它反映了变量间潜在的依赖关系。使用贝叶斯网建模已成为解决许多不确定性问题的强有力工具。基于国内外最新的研究成果对贝叶斯网模型的学习、推理和应用情况进行了综述,并对未来的发展方向进行了展望。     

Structure of the high-order Boltzmann machine from independencemaps

In this paper we consider the determination of the structure of the high-order Boltzmann machine (HOBM), a stochastic recurrent network for approximating probability distributions. We obtain the structure of the HOBM, the hypergraph of connections, from conditional independences of the probability distribution to model. We assume that an expert provides these conditional independences and from them we build independence maps, Markov and Bayesian networks, which represent conditional independences through undirected graphs and directed acyclic graphs respectively. From these independence maps we construct the HOBM hypergraph. The central aim of this paper is to obtain a minimal hypergraph. Given that different orderings of the variables provide in general different Bayesian networks, we define their intersection hypergraph. We prove that the intersection hypergraph of all the Bayesian networks (N!) of the distribution is contained by the hypergraph of the Markov network, it is more simple, and we give a procedure to determine a subset of the Bayesian networks that verifies this property. We also prove that the Markov network graph establishes a minimum connectivity for the hypergraphs from Bayesian networks.     

LEARNING STRUCTURED BAYESIAN NETWORKS: COMBINING ABSTRACTION HIERARCHIES AND TREE-STRUCTURED CONDITIONAL PROBABILITY TABLES

Context-specific independence representations, such as tree-structured conditional probability distributions, capture local independence relationships among the random variables in a Bayesian network (BN). Local independence relationships among the random variables can also be captured by using attribute-value hierarchies to find an appropriate abstraction level for the values used to describe the conditional probability distributions. Capturing this local structure is important because it reduces the number of parameters required to represent the distribution. This can lead to more robust parameter estimation and structure selection, more efficient inference algorithms, and more interpretable models. In this paper, we introduce Tree-Abstraction-Based Search (TABS), an approach for learning a data distribution by inducing the graph structure and parameters of a BN from training data. TABS combines tree structure and attribute-value hierarchies to compactly represent conditional probability tables. To construct the attribute-value hierarchies, we investigate two data-driven techniques: a global clustering method, which uses all of the training data to build the attribute-value hierarchies, and can be performed as a preprocessing step; and a local clustering method, which uses only the local network structure to learn attribute-value hierarchies. We present empirical results for three real-world domains, finding that (1) combining tree structure and attribute-value hierarchies improves the accuracy of generalization, while providing a significant reduction in the number of parameters in the learned networks, and (2) data-derived hierarchies perform as well or better than expert-provided hierarchies.     

基于先验节点序学习贝叶斯网络结构的优化方法

针对小样本数据集下学习贝叶斯网络 (Bayesian networks, BN)结构的不足, 以及随着条件集的增大, 利用统计方法进行条件独立 (Conditional independence, CI) 测试不稳定等问题, 提出了一种基于先验节点序学习网络结构的优化方法. 新方法通过定义优化目标函数和可行域空间, 首次将贝叶斯网络结构学习问题转化为求解目标函数极值的数学规划问题, 并给出最优解的存在性及唯一性证明, 为贝叶斯网络的不断扩展研究提出了新的方案. 理论证明以及实验结果显示了新方法的正确性和有效性.     

新的贝叶斯网络结构学习方法

贝叶斯网络是一种将贝叶斯概率方法和有向无环图的网络拓扑结构有机结合的表示模型,它描述了数据项及数据项之间的非线性依赖关系.报告了贝叶斯网络研究的现状,并针对传统算法需要主观规定网络中结点顺序的缺点,提出了一个新的可以在无约束条件下,根据观测得到的训练样本集的概率关系,自动完成学习贝叶斯网络结构的新方法.     

Exploiting causal independence in large Bayesian networks

The assessment of a probability distribution associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions based on the notion of causal independence have therefore been proposed, as these allow defining a probability distribution in terms of Boolean combinations of local distributions. However, for very large networks even this approach becomes infeasible: in Bayesian networks which need to model a large number of interactions among causal mechanisms, such as in fields like genetics or immunology, it is necessary to further reduce the number of parameters that need to be assessed. In this paper, we propose using equivalence classes of binomial distributions as a means to define very large Bayesian networks. We analyse the behaviours obtained by using different symmetric Boolean functions with these probability distributions as a means to model joint interactions. Some surprisingly complicated behaviours are obtained in this fashion, and their intuitive basis is examined.