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1.
贝叶斯网络是人工智能中不确定知识表示和推理的有力工具.介绍了贝叶斯网络的概念,给出一个实例,分析了贝叶斯网络推理的方法和过程. 相似文献
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贝叶斯网络是人工智能中不确定知识表示和推理的有力工具。介绍了贝叶斯网络的概念,给出一个实例,分析了贝叶斯网络推理的方法和过程。 相似文献
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提出了应用贝叶斯统计方法在分布式数据库MCDB上处理超大规模数据的实现方法,并以贝叶斯线性回归、话题模型的LDA和狄利克雷过程的聚类算法为例进行了论证.用户可以通过SQL语言定义变量之间的关系进行模拟.探索了一种使用简洁的SQL设计大规模统计学习系统的方法,其利用MCDB能够自动解决并行化和资源优化问题,以获得高性能的并行处理能力. 相似文献
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多模块贝叶斯网络中推理的简化 总被引:3,自引:0,他引:3
多模块贝叶斯网络(MSBN)引入了模块化和面向对象思想,是复杂大系统建模的有力工具.目前,如何简化MSBN中局部和全局推理的时空复杂度已成为影响其应用的关键问题.首先分析了用于局部贝叶斯网络推理的两类经典算法的时空复杂度,证明了它们本质上的一致性,并给出了统一的理论解释;进而用实验证明了影响推理复杂度的决定性因素是网络模型相应导出图的导出宽度,并指出了可以精确推理的贝叶斯网络族.最后,分析了降低MSBN全局推理复杂度的可行性,给出了简化MSBN全局推理的指导性原则. 相似文献
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Active Management Framework for Distributed Multimedia Systems 总被引:17,自引:0,他引:17
Ehab Al-Shaer 《Journal of Network and Systems Management》2000,8(1):49-72
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Hasina Abdu Hanan Lutfiyya Michael A. Bauer 《Journal of Network and Systems Management》2002,10(4):505-530
With the increased availability and complexity of distributed systems comes a greater need for solutions to assist in the management of distributed systems. Despite the significant contributions made towards the development of management tools that monitor and control distributed systems, little has been done to address issues such as optimizing the execution of management functions with respect to system and management requirements. This paper presents a management optimization model in which management agents and managed objects are efficiently configured on the basis of a set of system and management requirements. We illustrate our model and describe its implementation through a Branch- and Bound-based algorithm and a web-based interface. The latter enables users to specify the requirements used by the optimization algorithm to determine efficient management configurations. It also includes an XML-based interface through which management agents can be started independent of the underlying platforms. Performance characteristics of the proposed algorithm as well as experimental results to illustrate the validity of the model are also described. 相似文献
8.
Nevin Lianwen Zhang 《Applied Intelligence》1998,9(2):173-183
This paper studies computational properties of two exact inference algorithms for Bayesian networks, namely the clique tree propagation algorithm (CTP)1 and the variable elimination algorithm (VE). VE permits pruning of nodes irrelevant to a query while CTP facilitates sharing of computations among different queries. Experiments have been conducted to empirically compare VE and CTP. We found that, contrary to common beliefs, VE is often more efficient than CTP, especially in complex networks. 相似文献
9.
递归调节算法作为一种贝叶斯网络精确推理算法由于存储结果的个数是任意的,存储所占用的内存大小是可变的,所以该算法在空间上是自由的。然而当内存空间有限时,又希望节省计算时间,存储哪些计算结果就成了关键问题。针对此问题本文提出了应用深度优先分支定界法寻找存储占用空间的所有可能性,然后通过任意空间下推理时间的求解公式得到相应的推理时间,进而构造贝叶斯网络推理的时间—空间曲线,通过所构造的曲线找出最优离散存储策略,得到时间和空间之间的最佳的结合点,以最小时间代价换取了最大的存储空间。 相似文献
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基于贝叶斯网络的多阶段系统可靠性分析模型 总被引:4,自引:0,他引:4
针对多阶段系统(PMS)的可靠性评估问题,提出了一种基于贝叶斯网络(BN)的可靠性分析模型PMS-BN.PMS-BN模型首先为每个阶段构建各自的BN,其结果命名为phase-BN.为了描述阶段之间的相关性,将所有phase-BN中表示同一部件但属于不同阶段的根节点用有向边连接,并且将所有phase-BN中的叶节点与一个新的表示PMS系统的节点用有向边连接,从而构建出用于刻画PMS系统的BN,称之为PMS-BN.将各个阶段时间离散为m个时间段,利用BN推理算法获得PMS的可靠性参数.通过2个实例详细阐述PMS-BN的建模过程.PMS-BN模型为PMS可靠性分析提供了一种新的策略,能够方便地实施系统可靠度计算、故障诊断、重要度分析等应用.若构建的PMS-BN满足所有非根节点均具有2个父节点,则PMS可靠度的求解过程仅需O(Nm3)的计算复杂度,其中N为非根节点的个数. 相似文献
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Hanan L. Lutfiyya Michael A. Bauer Andrew D. Marshall David K. Stokes 《Journal of Network and Systems Management》2000,8(4):499-525
Managing the availability and performance of a distributed system involves monitoring the behavior of the system, identifying system problems, and correcting those problems. Each of these tasks requires some expertise, such as an understanding of the mechanics of the underlying system components. As the size and complexity of these systems increases, and the number of distributed applications executing on these systems increases, managing the availability and performance of distributed systems becomes more difficult. Little research has focused on embedding systems management expertise into a management application for a distributed system. In this paper we describe a rule-based management application for a commercially available distributed computing environment that is capable of monitoring the distributed system, detecting system service-related performance and availability problems, and generating corrective actions to correct the problems. 相似文献
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Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input 下载免费PDF全文
Long Chen Linqing Wang Zhongyang Han Jun Zhao Wei Wang 《IEEE/CAA Journal of Automatica Sinica》2020,7(5):1437-1445
Prediction intervals (PIs) for industrial time series can provide useful guidance for workers. Given that the failure of industrial sensors may cause the missing point in inputs, the existing kernel dynamic Bayesian networks (KDBN), serving as an effective method for PIs construction, suffer from high computational load using the stochastic algorithm for inference. This study proposes a variational inference method for the KDBN for the purpose of fast inference, which avoids the time-consuming stochastic sampling. The proposed algorithm contains two stages. The first stage involves the inference of the missing inputs by using a local linearization based variational inference, and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices. To verify the effectiveness of the proposed method, a synthetic dataset and a practical dataset of generation flow of blast furnace gas (BFG) are employed with different ratios of missing inputs. The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one. 相似文献
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Lee Chain-Wu Huang Chun-Hsi Yang Laurence Tianruo Rajasekaran Sanguthevar 《The Journal of supercomputing》2004,29(2):211-227
This paper presents the task model, instruction set, reasoning scheme, software infrastructure, as well as the experimental results, of a new distributed semantic network system. Unlike the synchronous and static marker passing algorithm previously used for parallel semantic network design, our system operates asynchronously, supporting knowledge sharing, dynamic load balancing and duplicate checking. To better the performance in distributed environments, the system has two collaborating components: the slave module, which performs task execution; and the host module, which interacts with the user and processes the information for the slaves. Our current implementation focuses on path-based knowledge inferences, using ANSI C and the MPICH-G2 with flex lexical analyzer and the yacc parser generator. Tests of individual components have been performed on a SUN multiprocessor server. The experiments demonstrate promising speedups. 相似文献
14.
William J. McCausland 《Computational Economics》2004,23(3):201-218
The BACC software provides its users with tools for Bayesian Analysis,Computation and Communications. The current version of the software, describedhere, implements these tools as extensions to popular mathematicalapplications such as Matlab, S-PLUS, R, and Gauss, running under Windows,Linux or Unix. From the user's perspective, there is a seamless integrationof special-purpose BACC commands for posterior simulation and related taskswith powerful built-in commands for matrix computation, graphics, program flowcontrol, and I/O. Examples demonstrate the use of the software within Matlab.Nineteen models are currently available, and many others are planned. BACC isdesigned to be extendible, not only by the developers of BACC, but also byothers who wish to implement their own models and thus make them available toBACC users. While model development requires programming in C, several designfeatures of BACC facilitate this development. BACC is freely available athttp://www2.cirano.qc.ca/bacc/. 相似文献
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用于态势评估中因果推理的贝叶斯网络 总被引:4,自引:0,他引:4
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。 相似文献
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We present a collective approach to learning a Bayesian network from distributed heterogeneous data. In this approach, we first learn a local Bayesian network at each site using the local data. Then each site identifies the observations that are most likely to be evidence of coupling between local and non-local variables and transmits a subset of these observations to a central site. Another Bayesian network is learnt at the central site using the data transmitted from the local site. The local and central Bayesian networks are combined to obtain a collective Bayesian network, which models the entire data. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.16 November 2001 相似文献
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变结构动态贝叶斯网络的机制研究 总被引:1,自引:0,他引:1
传统的动态贝叶斯网络(Dynamic Bayesian networks, DBNs)描述的是一个稳态过程,而处理非稳态过程,变结构动态贝叶斯网络更适 用、更灵活、更有效.为了克服现有变结构离散 动态贝叶斯网络推理算法只能处理硬证据的缺陷,本文在深入分析变结构动态贝叶斯网络机制及其特 征的基础上,提出了变结构离散动态贝叶斯网络的 快速推理算法.此外,对变结构动态贝叶斯网络的特例,即数据缺失动态贝叶斯网络进行了定义并构建 了相应的模型.仿真实验验证了变结构离散动态贝 叶斯网络快速推理算法的有效性及计算效率. 相似文献
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考虑将广义超级市场模型(Generalized Supermarket Model,GSM)应用于分布式视频信息存储网络的任务均衡分配。设:①分布式视频信息存储网络由在地理上分布的存储服务结点、存储负载均衡调度结点和客户结点组成;③视频信息存储任务的到这是一泊松过程,任务粒度为视频流文件;③存储任务的执行时间服从指数分布;④系统在初始时刻的存储任务有限;⑤与存储任务的执行时间相比,存储任务的调度时间不计。在概率意义下,深入讨论了基于广义超级市场模型的负载均衡调度与随机服务选择两种情况下的平均存储任务消耗时间,分析结果表明:在大规模分布式视频信息存储网络中,采用广义超级市场模型的存储任务均衡调度可显著提高系统的平均存储任务消耗时间性能。 相似文献
19.
基于三维特征参数的贝叶斯推理电路功耗模型 总被引:1,自引:0,他引:1
在功耗与信号统计分析的基础上,采用贝叶斯推理技术建立周期精确的功耗宏模型.通过分析信号特征对电路功耗的影响,选择输入信号密度Pin、输入跳变密度Din和输出跳变密度Dout作为贝叶斯推理的三维特征参数,证明了上述特征参数对信号时间和空间相关性信息的覆盖.实验结果表明,该方法较目前的门级功耗分析速度提高400余倍,周期功耗平均误差可以控制在10%以内. 相似文献