首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献,搜索用时 15 毫秒
1.
This paper presents three mathematical models for the distribution of message lengths in overseas communications. The results and conclusions presented in this paper are based on the outcome of a random experiment performed at Overseas Communication Service Centre, Bombay, India. A sample of sufficiently large size (about 21,000 messages) was selected so that the sample mean does not deviate more than 5% of the population mean at a level of confidence more than 95%. Three different models namely (1) Geometrical distribution with a constant of 7 words, (2) Compound distribution obtained as a weighted sum of a geometrical distribution with a constant of 7 words and an impulse function with impulse at 22 words, and (3) Compound distribution obtained as a weighted sum of two geometrical distributions with constants of 7 and 22 words respectively, were assumed to be the representatives of experimental distribution of message lengths. Model parameters were estimated using the method of least squares. Box method was used to solve the minimization problem. Chi-square and Kolmogorov-Smirnov tests were then applied to test for goodness-of-fit of the three assumed models. The K-S test results revealed that all three models are acceptable whereas chi-square test results revealed that the first two models are not supported by the experimental data and hence are rejected but the last model is supported by the data and is an acceptable probability model of message lengths in overseas communications.The developed models are useful in optimal design of computer communication systems such as data multiplexing and voice-data systems.  相似文献   

2.
The proposed study investigates a continuous review inventory model with order quantity, reorder point, backorder price discount, process quality, and lead time as decision variables. An investment function is used to improve the process quality. Two models are developed based on the probability distribution of lead time demand. The lead time demand follows a normal distribution in the first model and in the second model it does not follow any specific distribution but mean and standard deviation are known. We prove two lemmas to obtain optimal solutions for the normal distribution model and distribution free model. Finally, some numerical examples are given to illustrate the model.  相似文献   

3.
The product of experts learning procedure can discover a set of stochastic binary features that constitute a nonlinear generative model of handwritten images of digits. The quality of generative models learned in this way can be assessed by learning a separate model for each class of digit and then comparing the unnormalized probabilities of test images under the 10 different class-specific models. To improve discriminative performance, a hierarchy of separate models can be learned, for each digit class. Each model in the hierarchy learns a layer of binary feature detectors that model the probability distribution of vectors of activity of feature detectors in the layer below. The models in the hierarchy are trained sequentially and each model uses a layer of binary feature detectors to learn a generative model of the patterns of feature activities in the preceding layer. After training, each layer of feature detectors produces a separate, unnormalized log probability score. With three layers of feature detectors for each of the 10 digit classes, a test image produces 30 scores which can be used as inputs to a supervised, logistic classification network that is trained on separate data  相似文献   

4.
针对带概率的迭代函数系统,伴随概率在吸引子图像控制中的影响作用,文章提出了几种不同的概率分布模型,应用该模型可以对吸引子图像实现局部细节和整体形状的控制,并以树木的模拟为实例,通过计算机数值实验展示了所给模型的控制效果。此方法用于计算机模拟自然景物,计算简单,易于操作,效果较好。  相似文献   

5.
The Domain Adaptation problem in machine learning occurs when the distribution generating the test data differs from the one that generates the training data. A common approach to this issue is to train a standard learner for the learning task with the available training sample (generated by a distribution that is different from the test distribution). One can view such learning as learning from a not-perfectly-representative training sample. The question we focus on is under which circumstances large sizes of such training samples can guarantee that the learned classifier preforms just as well as one learned from target generated samples. In other words, are there circumstances in which quantity can compensate for quality (of the training data)? We give a positive answer, showing that this is possible when using a Nearest Neighbor algorithm. We show this under some assumptions about the relationship between the training and the target data distributions (the assumptions of covariate shift as well as a bound on the ratio of certain probability weights between the source (training) and target (test) distribution). We further show that in a slightly different learning model, when one imposes restrictions on the nature of the learned classifier, these assumptions are not always sufficient to allow such a replacement of the training sample: For proper learning, where the output classifier has to come from a predefined class, we prove that any learner needs access to data generated from the target distribution.  相似文献   

6.
遗传算法中参数的选取决定遗传算法的运行性能.目前,对算法中参数选取都是经验性的.本文针对一个典型的2-bit问题,分析了在不同参数选取下GA的全局动力学形态.通过对标准遗传算法的各种参数的选取,分别建立了数学模型.分析了这些模型的吸引子,揭示了不同参数对动力学形态的影响.世代重叠模型和无参数模型的动力学形态相似.当变异概率很小时,模型与没有变异算子相类似;当变异算子足够大时,模型的动力学形态随着变异概率的增加发生了突变.原有的吸引不动点消失,原来的排斥不动点变成吸引不动点.这些论证为遗传算法中参数选取提供了一些理论上的证据.  相似文献   

7.
In semiparametric regression models, penalized splines can be used to describe complex, non-linear relationships between the mean response and covariates. In some applications it is desirable to restrict the shape of the splines so as to enforce properties such as monotonicity or convexity on regression functions. We describe a method for imposing such shape constraints on penalized splines within a linear mixed model framework. We employ Markov chain Monte Carlo (MCMC) methods for model fitting, using a truncated prior distribution to impose the requisite shape restrictions. We develop a computationally efficient MCMC sampler by using a correspondingly truncated multivariate normal proposal distribution, which is a restricted version of the approximate sampling distribution of the model parameters in an unconstrained version of the model. We also describe a cheap approximation to this methodology that can be applied for shape-constrained scatterplot smoothing. Our methods are illustrated through two applications, the first involving the length of dugongs and the second concerned with growth curves for sitka spruce trees.  相似文献   

8.
Model-based image segmentation has been extensively used in medical imaging to learn both the shape and appearance of anatomical structures from training datasets. The more training datasets are used, the more accurate is the segmented model, as we account for more information about its variability. However, training datasets of large size with a proper sampling of the population may not always be available. In this paper, we compare the performance of statistical models in the context of lower limb bones segmentation using MR images when only a small number of datasets is available for training. For shape, both PCA-based priors and shape memory strategies are tested. For appearance, methods based on intensity profiles are tested, namely mean intensity profiles, multivariate Gaussian distributions of profiles and multimodal profiles from EM clustering. Segmentation results show that local and simple methods perform the best when a small number of datasets is available for training. Conversely, statistical methods feature the best segmentation results when the number of training datasets is increased.  相似文献   

9.
Safety and reliability have become important software quality characteristics in the development of safety-critical software systems. However, there are so far no quantitative methods for assessing a safety-critical software system in terms of the safety/reliability characteristics. The metrics of software safety is defined as the probability that conditions that can lead to hazards do not occur. In this paper, we propose two stochastic models for software safety/reliability assessment: the data-domain dependent safety assessment model and the availability-related safety assessment model. These models focus on describing the time- or execution-dependent behavior of the software faults which can lead to unsafe states when they cause software failures. The application of one of these models to optimal software release problems is also discussed. Finally, numerical examples are illustrated for quantitative software safety assessment and optimal software release policies. This revised version was published online in June 2006 with corrections to the Cover Date.  相似文献   

10.
对以固定强度嵌入的乘性扩频水印的最优检测模型进行了研究.分别利用整体参数估计和局部参数估计方法对DFT、DCT和DWT域内最优检测的分布模型参数进行了估计,并就这两种参数估计的最优检测方法做出比较.通过实验得到以下结论:在无攻击条件下,局部参数估计的性能优于整体参数估计的性能;在有攻击条件下,整体估计的性能要好于局部参数估计的性能;压缩攻击会导致水印误检概率的增加,不同的攻击对模型造成的影响不同.  相似文献   

11.
A framework for reformulating input design problems in prediction error identification as convex optimization problems is presented. For linear time-invariant single input/single output systems, this framework unifies and extends existing results on open-loop input design that are based on the finite dimensional asymptotic covariance matrix of the parameter estimates. Basic methods for parametrizing the input spectrum are provided and conditions on these parametrizations that guarantee that all possible covariance matrices for the asymptotic distribution of the parameter estimates can be generated are provided. A wide range of model quality constraints can be handled. In particular, different frequency-by-frequency constraints can be used. This opens up new applications of input design in areas such as robust control. Furthermore, quality specifications can be imposed on all models in a confidence region. Thus, allowing for statements such as "with at least 99% probability the model quality specifications will be satisfied".  相似文献   

12.
《Advanced Robotics》2013,27(5):459-476
Static error propagation in positioning robot arms has been extensively analyzed based on linearized error propagation models. These models are used for robot design and calibration. Most results were calculated using normally distributed errors in the different robot joints and links. In reality not all joints and links errors are normally distributed, and a mix of suitable symmetric and nonsymmetric probability distributions should be used in the error propagation model. The paper presents the general model for propagating errors with different distributions applicable to any system whose model can be linearized. Numerical examples are given for a general beta distribution of errors, and for a mix of symmetric and nonsymmetric distributions. Within the work-volume, the different configurations that a robot arm may attain during its life span can be expressed by a probability distribution. The paper includes the analysis of the effect of this distribution on the position error of the end-effector.  相似文献   

13.
The Strut-and-Tie Method is considered a basic tool for analysis and design of reinforced concrete structures and has been incorporated in different codes of practice such as: EC-2, BS 8110, ACI 318-08, EHE-08, etc. The stress trajectories or load path methods have been used to generate strut-and-tie models. However, the models produced by these methods are not unique, with the result depending on the intuition or expertise of the designer, specifically with regards to region D of the structure, where the load path distribution is non-linear. Topology optimization can offer new opportunities to eliminate the limitations of traditional methods. The aim of this work was to study the effect of using different mechanical properties for the steel reinforcement and for the concrete on the emerging topology of strut-and-tie models. The Isolines Topology Design (ITD) method was used for this research. Three examples are presented to show the effect of different mechanical properties used for the tensile (steel) and compressive (concrete) regions of the structure, the: (1) Single short corbel; (2) Deep beam with opening; and (3) Double-sided beam-to-column joint.  相似文献   

14.
标记分布学习是近年来提出的一种新的机器学习范式,它能很好地解决某些标记多义性的问题。现有的标记分布学习算法均利用条件概率建立参数模型,但未能充分利用特征和标记间的联系。本文考虑到特征相似的样本所对应的标记分布也应当相似,利用原型聚类的k均值算法(k-means),将训练集的样本进行聚类,提出基于k-means算法的标记分布学习(label distribution learning based on k-means algorithm,LDLKM)。首先通过聚类算法k-means求得每一个簇的均值向量,然后分别求得对应标记分布的均值向量。最后将测试集和训练集的均值向量间的距离作为权重,应用到对测试集标记分布的预测上。在6个公开的数据集上进行实验,并与3种已有的标记分布学习算法在5种评价指标上进行比较,实验结果表明提出的LDLKM算法是有效的。  相似文献   

15.
A validated simulation model primarily requires performing an appropriate input analysis mainly by determining the behavior of real-world processes using probability distributions. In many practical cases, probability distributions of the random inputs vary over time in such a way that the functional forms of the distributions and/or their parameters depend on time. This paper answers the question whether a sequence of observations from a process follow the same statistical distribution, and if not, where the exact change points are, so that observations within two consecutive change points follow the same distribution. We propose two different methods based on likelihood ratio test and cluster analysis to detect multiple change points when observations follow non-stationary Poisson process with diverse occurrence rates over time. Results from a comprehensive Monte Carlo study indicate satisfactory performance for the proposed methods. A well-known example is also considered to show the application of our findings in real world cases.  相似文献   

16.
A treatment for temporal-spatial data such as atmospheric temperature using an information-statistical approach is proposed. Conditioning on specific spatial nature of the data, the temporal aspect of the data is first modeled parametrically as Gaussian, and Schwarz information criterion is then applied to detect multiple mean change points—thus the Gaussian statistical models—to account for changes of the population mean over time. To examine the spatial characteristics of the data, successive mean change points are qualified by finite categorical values. The distribution of the finite categorical values is then used to estimate a non-parametric probability model through a non-linear SVD-based optimization approach; where the optimization criterion is Shannon expected entropy. This optimal probability model accounts for the spatial characteristics of the data and is then used to derive spatial association patterns subject to chi-square statistic hypothesis test. The proposed approach is applied to examine the weather data set obtained from NOAA. Selected temperature data are studied. These data cover different geographical localities in the United States, with some spanning over 200 years. Preliminary results are reported.  相似文献   

17.
The change detection and segmentation methods have gained considerable attention in scientific research and appear to be the central issue in various application areas. The objective of the paper is to present a segmentation method, based on maximum a posteriori probability (MAP) estimator, with application in seismic signal processing; some interpretations and connections with other approaches in change detection and segmentation, as well as computational aspects in this field are also discussed. The experimental results obtained by Monte Carlo simulations for signal segmentation using different signal models, including models with changes in the mean, in FIR, AR and ARX model parameters, as well as comparisons with other methods, are presented and the effectiveness of the proposed approach is proved. Finally, we discuss an application of segmentation in the analysis of the earthquake records during the Kocaeli seism, Turkey, August 1999, Arcelik station (ARC). The optimal segmentation results are compared with time–frequency analysis, for the reduced interference distribution (RID). The analysis results confirm the efficiency of the segmentation approach used, the change instants resulted by MAP appearing clear in energy and frequency contents of time–frequency distribution.  相似文献   

18.
目的 似物性推荐为近年来提出的一种快速物体定位方法,而参数最小割模型作为似物性推荐的一种重要模型受到广泛关注。针对传统的参数最小割模型受颜色分布影响较大的问题,提出融合多个具有信息互补作用的外形先验予以改进。方法 首先构造了一种数据驱动的基于形状共享的外形先验,以发现具有相似外形的物体区域;其次,从格式塔完形心理学的角度入手,引出了一种测地星形凸面性的外形先验,约束外形的拓扑结构,生成外形不同的物体区域;最后,结合外形先验、颜色分布、边缘响应强度以及尺度线索,构建能量函数以表征新的模型,从而增强模型对复杂颜色分布的鲁棒性。结果 分别在Seg VOC12和BSDS300数据集中进行了外形先验有效性验证、复杂颜色分布下算法鲁棒性分析和前沿似物性推荐算法对比分析等实验,结果表明,本文采用融合互补性外形先验能提高候选区域定位精度,具有更好的颜色分布鲁棒性,当颜色简单性位于[0.7,,08]之间时,算法结合外形先验后平均最佳重叠率最高可达到9.8%的提升,且在与13种具有代表性的似物性推荐算法进行区域级物体定位能力对比实验中,本文算法在不同的重叠率阈值下均达到了相近的查全率。结论 本文算法具有更高的前景与背景的区分能力,能够适应各种复杂颜色分布,同时具有较好的物体定位能力。  相似文献   

19.
Decision support tools are increasingly used in operations where key decision inputs such as demand, quality, or costs are uncertain. Often such uncertainties are modeled with probability distributions, but very little attention is given to the shape of the distributions. For example, state-of-the-art planning systems have weak, if any, capabilities to account for the distribution shape. We consider demand uncertainties of different shapes and show that the shape can considerably change the optimal decision recommendations of decision models. Inspired by discussions with a leading consumer electronics manufacturer, we analyze how four plausible demand distributions affect three representative decision models that can be employed in support of inventory management, supply contract selection and capacity planning decisions. It is found, for example, that in supply contracts flexibility is much more appreciated if demand is negatively skewed, i.e., has downside potential, compared to positively skewed demand. We then analyze the value of distributional information in the light of these models to find out how the scope of improvement actions that aim to decrease demand uncertainty vary depending on the decision to be made. Based on the results, we present guidelines for effective utilization of probability distributions in decision models for operations management.  相似文献   

20.
Ji  Haijin  Huang  Song  Wu  Yaning  Hui  Zhanwei  Zheng  Changyou 《Software Quality Journal》2019,27(3):923-968

Software defect prediction (SDP) plays a significant part in identifying the most defect-prone modules before software testing and allocating limited testing resources. One of the most commonly used classifiers in SDP is naive Bayes (NB). Despite the simplicity of the NB classifier, it can often perform better than more complicated classification models. In NB, the features are assumed to be equally important, and the numeric features are assumed to have a normal distribution. However, the features often do not contribute equivalently to the classification, and they usually do not have a normal distribution after performing a Kolmogorov-Smirnov test; this may harm the performance of the NB classifier. Therefore, this paper proposes a new weighted naive Bayes method based on information diffusion (WNB-ID) for SDP. More specifically, for the equal importance assumption, we investigate six weight assignment methods for setting the feature weights and then choose the most suitable one based on the F-measure. For the normal distribution assumption, we apply the information diffusion model (IDM) to compute the probability density of each feature instead of the acquiescent probability density function of the normal distribution. We carry out experiments on 10 software defect data sets of three types of projects in three different programming languages provided by the PROMISE repository. Several well-known classifiers and ensemble methods are included for comparison. The final experimental results demonstrate the effectiveness and practicability of the proposed method.

  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号