检索结果-内蒙古大学图书馆

modelling the spatial distribution of heavy vehicle loads on long-span bridges based on undirected graphical model

STRUCTURE AND INFRASTRUCTURE ENGINEERING 2019年第11期15卷 1485-1499页

作者： Chen, Zhicheng Bao, Yuequan Chen, Jiahui Li, Hui Harbin Inst Technol Minist Ind & Informat Technol Key Lab Intelligent Disaster Mitigat Harbin Heilongjiang Peoples R China Harbin Inst Technol Minist Educ Key Lab Struct Dynam Behav & Control Harbin Heilongjiang Peoples R China Harbin Inst Technol Sch Civil Engn Harbin Heilongjiang Peoples R China

Vehicle load modelling is highly important for bridge design and safety evaluation. Conventional modelling approaches for vehicle loads have limitations in characterizing the spatial distribution of vehicles. This article presents a probabilistic method for modelling the spatial distribution of heavy vehicle loads on long-span bridges by using the undirected graphical model (UGM). The bridge deck is divided into grid cells, a UGM with each node corresponding to each cell is employed to model the location distribution of heavy vehicles, by which probabilities of heavy-vehicle distribution patterns can be efficiently calculated through applying the junction tree algorithm. A Bayesian inference method is also developed for updating the location model in consideration of the non-stationarity of traffic process. Gross weights of heavy vehicles are modelled by incorporating additional random variables to the vehicle-location UGM, corresponding probability distributions are constructed conditioned on ignoring correlation and considering correlation, respectively. Case studies using simulated data as well as field monitoring data have been conducted to examine the method. Compared with previous studies involving vehicle load modelling, the presented method can implement probabilistic analysis for all spatial distribution patterns of heavy vehicles on the entire bridge deck.

关键词： Bridge loads bridges long-span heavy vehicles probabilistic methods probability distribution undirected graphical model

来源：评论

学校读者我要写书评

暂无评论

The multimedia recommendation algorithm based on probability graphical model

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 19035-19050页

作者： Li, Chen Li, Yu Wang, Chunhua Dong, Shifeng Gao, Haofei Zhao, Qian Wu, Wei Commun Univ China Beijing Peoples R China Beijing Aerosp Changfeng Sci & Ind Grp Beijing Peoples R China

In the multimedia big data, the demand for personalized multimedia recommendation algorithm is increasing to ease the multimedia information overload. The multimedia recommendation system has been applied in various industries and has been playing a significant role. With the development of multimedia big data, developing multimedia recommendation algorithms can effectively be used in multimedia data. However, a large number of prevailing recommendation systems cannot meet the multimedia recommendation requirements, since they ignore the user-item interactions with multimedia content. This essay realizes the multimedia recommendation based on probability graphical model, to deal with the cold start and data sparsity involved in collaborative filtering recommendation, proposing that add the user tag to user-item model. The essay optimizes the multimedia recommendation algorithm based on undirected graphical model and tests it with singular value decomposition, clustering and Naive Bayes separately. The essay also builds the checklist recommendation model and experiments extensively for comparison with the conditional multimedia recommendation algorithm, by using PersonalRank algorithm based on random-walk to work out the weight coefficient of the user tag. At the same time, the essay enhances the probability-graph multimedia recommendation algorithm by dimensionality reduction and clustering, with the result of noticeably improved precision and recall.

关键词： Multimedia recommendation Probability graphical model undirected graphical model Collaborative filtering Bayesians Network

来源：评论

学校读者我要写书评

暂无评论

Negative binomial graphical model with excess zeros

引用

STATISTICAL ANALYSIS AND DATA MINING 2021年第5期14卷 449-465页

作者： Park, Beomjin Choi, Hosik Park, Changyi Univ Seoul Dept Stat Seoul South Korea Univ Seoul Grad Sch Dept Urban Big Data Convergence Seoul South Korea

Markov random field or undirected graphical models (GM) are a popular class of GM useful in various fields because they provide an intuitive and interpretable graph expressing the complex relationship between random variables. The zero-inflated local Poisson graphical model has been proposed as a graphical model for count data with excess zeros. However, as count data are often characterized by over-dispersion, the local Poisson graphical model may suffer from a poor fit to data. In this paper, we propose a zero-inflated local negative binomial (NB) graphical model. Due to the dependencies of parameters in our models, a direct optimization of the objective function is difficult. Instead, we devise expectation-minimization algorithms based on two different parametrizations for the NB distribution. Through a simulation study, we illustrate the effectiveness of our method for learning network structure from over-dispersed count data with excess zeros. We further apply our method to real data to estimate its network structure.

关键词： count data Markov random field over-dispersion undirected graphical model zero inflation

来源：评论

学校读者我要写书评

暂无评论

Estimation of nonparanormal graphical models based on ranked set sampling (RSS)

引用

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2023年第6期93卷 932-955页

作者： Aghabozorgi, Hamid Haji Eskandari, Farzad Allameh Tabatabai Univ Dept Stat Tehran Iran

Gaussian graphical model, which assumes that the variables of interest jointly follow a multivariate normal distribution with a sparse precision matrix, has been widely used to study the intrinsic dependence between the variables. However, we often encounter non-normal data, and we use variable transformation to achieve normality. Motivated by sparse additive models, the nonparanormal model extends the Gaussian graphical model to semiparametric Gaussian copula models, in which it is assumed that the variables follow a joint multivariate normal distribution after a set of unknown smooth monotone transformations. The basic methods of estimating undirected graphs in high-dimensional settings apply the simple random sampling (SRS) method to estimate the parameters of the model. We consider the ranked set sampling (RSS) approach for estimating the nonparanormal graphical model. Computationally, we show that using RSS leads to a better inference about the unknown features of the nonparanormal graphical model, and the proposed rank-based estimator performs as well as its counterparts estimators defined based on SRS. We study the numerical performance of the proposed method through a simulation study under both ideal and noisy settings and apply it to a genomic data set.

关键词： Gaussian copula nonparanormal model ranked set sampling undirected graphical model

来源：评论

学校读者我要写书评

暂无评论

Bayesian model selection for high-dimensional Ising models, with applications to educational data

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2022年 165卷 107325-107325页

作者： Park, Jaewoo Jin, Ick Hoon Schweinberger, Michael Yonsei Univ Dept Stat & Data Sci Seoul South Korea Yonsei Univ Dept Appl Stat Seoul South Korea Univ Missouri Dept Stat Columbia MO 65211 USA

Doubly-intractable posterior distributions arise in many applications of statistics concerned with discrete and dependent data, including physics, spatial statistics, machine learning, the social sciences, and other fields. A specific example is psychometrics, which has adapted high-dimensional Ising models from machine learning, with a view to studying the interactions among binary item responses in educational assessments. To estimate high-dimensional Ising models from educational assessment data, l(1)-penalized nodewise logistic regressions have been used. Theoretical results in high-dimensional statistics show that l(1)-penalized nodewise logistic regressions can recover the true interaction structure with high probability, provided that certain assumptions are satisfied. Those assumptions are hard to verify in practice and may be violated, and quantifying the uncertainty about the estimated interaction structure and parameter estimators is challenging. We propose a Bayesian approach that helps quantify the uncertainty about the interaction structure and parameters without requiring strong assumptions, and can be applied to Ising models with thousands of parameters. We demonstrate the advantages of the proposed Bayesian approach compared with l(1)-penalized nodewise logistic regressions by simulation studies and applications to small and large educational data sets with up to 2,485 parameters. Among other things, the simulation studies suggest that the Bayesian approach is more robust against model misspecification due to omitted covariates than l(1)-penalized nodewise logistic regressions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Bayesian model selection Doubly intractable posterior distribution Ising model undirected graphical model Psychometrics

来源：评论

学校读者我要写书评

暂无评论

On generating random Gaussian graphical models

引用

INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2020年 125卷 240-250页

作者： Cordoba, Irene Varando, Gherardo Bielza, Concha Larranaga, Pedro Univ Politecn Madrid Madrid Spain Univ Copenhagen Copenhagen Denmark

Structure learning methods for covariance and concentration graphs are often validated on synthetic models, usually obtained by randomly generating: (i) an undirected graph, and (ii) a compatible symmetric positive definite (SPD) matrix. In order to ensure positive definiteness in (ii), a dominant diagonal is usually imposed. In this work we investigate different methods to generate random symmetric positive definite matrices with undirected graphical constraints. We show that if the graph is chordal it is possible to sample uniformly from the set of correlation matrices compatible with the graph, while for general undirected graphs we rely on a partial orthogonalization method. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Concentration graph Covariance graph Positive definite matrix simulation undirected graphical model Algorithm validation

来源：评论

学校读者我要写书评

暂无评论

Estimating finite mixture of continuous trees using penalized mutual information

引用

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2020年第20期49卷 4974-4987页

作者： Khalili, Atefeh Eskandari, Farzad Allameh Tabatabai Univ Dept Stat Tehran Iran

In this paper we introduce continuous tree mixture model that is the mixture of undirected graphical models with tree structured graphs and is considered as multivariate analysis with a non parametric approach. We estimate its parameters, the component edge sets and mixture proportions through regularized maximum likalihood procedure. Our new algorithm, which uses expectation maximization algorithm and the modified version of Kruskal algorithm, simultaneosly estimates and prunes the mixture component trees. Simulation studies indicate this method performs better than the alternative Gaussian graphical mixture model. The proposed method is also applied to water-level data set and is compared with the results of Gaussian mixture model.

关键词： Tree mixture model penalized maximum likelihood continuous multivariate analysis non parametric inference mixture model graphical mixture model undirected graphical model

来源：评论

学校读者我要写书评

暂无评论

Path weights in concentration graphs

引用

BIOMETRIKA 2020年第3期107卷 705-722页

作者： Roverato, Alberto Castelo, Robert Univ Padua Dept Stat Sci Via C Battisti 241 I-35121 Padua Italy Univ Pompeu Fabra Dept Expt & Hlth Sci Dr Aiguader 88 E-08003 Barcelona Spain

A graphical model provides a compact and efficient representation of the association structure in a multivariate distribution by means of a graph. Relevant features of the distribution are represented by vertices, edges and higher-order graphical structures such as cliques or paths. Typically, paths play a central role in these models because they determine the dependence relationships between variables. However, while a theory of path coefficients is available for directed graph models, little research exists on the strength of the association represented by a path in an undirected graph. Essentially, it has been shown that the covariance between two variables can be decomposed into a sum of weights associated with each of the paths connecting the two variables in the corresponding concentration graph. In this context, we consider concentration graph models and provide an extensive analysis of the properties of path weights and their interpretation. Specifically, we give an interpretation of covariance weights through their factorization into a partial covariance and an inflation factor. We then extend the covariance decomposition over the paths of an undirected graph to other measures of association, such as the marginal correlation coefficient and a quantity that we call the inflated correlation. Application of these results is illustrated with an analysis of dietary intake networks.

关键词： Concentration graph Covariance decomposition Inflation factor Partial correlation undirected graphical model undirected path

来源：评论

学校读者我要写书评

暂无评论

Hyperspectral Image Clustering based on Variational Expectation Maximization 11

Hyperspectral Image Clustering based on Variational Expectat...

引用

IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Jiao, Yuchen Ma, Yirong Gu, Yuantao Tsinghua Univ Dept Elect Engn Beijing Peoples R China 69010 Unit PLA Urumqi Peoples R China

ISBN: (纸本)9781728119465

Hyperspectral image clustering is an important and challenging problem, which aims to group image pixels according to the land cover information extracted from the spectrum. The spectrum observed at adjacent pixels are often highly-correlated, and leveraging such spatial correlation can greatly improve the clustering accuracy. Markov Random Field (MRF) is a powerful model to characterize such correlation. However, in this model the spatial parameter beta often needs to be manually tuned, which brings difficulty in finding an optimal value. In this paper, we propose a novel hyperspectral clustering algorithm, which is able to learn parameter beta from the data and thus achieves better performance. Specifically, we model the spectral information with Gaussian mixture model, and use variational expectation maximization method to complete the parameter estimation and clustering task. Experiments on both synthetic and real-world data sets verify the effectiveness of the proposed algorithm.

关键词： Hyperspectral Image clustering undirected graphical model variational expectation maximization spatial parameter

来源：评论

学校读者我要写书评

暂无评论

Exploration of Dependencies among Sections in a Supermarket Using a Tree-Structured undirected graphical model

Exploration of Dependencies among Sections in a Supermarket ...

引用

12th IEEE International Conference on Data Mining (ICDM)

作者： Takai, Keiji Kansai Univ Data Min Lab Osaka Japan

ISBN: (纸本)9780769549255;9781467351645

In research of purchase behavior in a supermarket, it is important to understand dependencies among sections of the supermarket, with each section corresponding to a category of items. An undirected graphical model is a powerful tool for this purpose. A problem with the application of an undirected graphical model is that there are many variables and, thus, a lot of computation is needed. In this article, we first apply a tree-structured undirected graphical model to reduce the computational amount, and second, propose a method to impose a restriction, based on our needs, on the tree-structured undirected graphical model. The variables we use are the length of time spent in each section and the number of items bought from each section. We found that some of the sections have influence on the adjacent sections and that some of the other sections have no influence on the adjacent sections, but do have influence on the nonadjacent sections. We also found that the number of items and the length of stationary time in the sections that influence a large number of sections are negatively related to those same variables in the other sections. Based on this result, managerial implications are described. Finally, we summarize this article and discuss some problems in the application of graphical models.

关键词： Dependency Purchase Behavior undirected graphical model Tree Structure Restriction

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：