检索结果-内蒙古大学图书馆

Intelligent design generation of plane pattern based on rjmcmc algorithm

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第1期25卷 752-765页

作者： Shi, Xiaoyun Chuzhou Polytech Sch Commun & Design Chuzhou Peoples R China

Pattern design has a wide range of applications in daily life, such as decoration design, web design, and so on. Common design methods include artificial design and intelligent design, and design methods based on intelligent technology tend to have higher efficiency. In order to make the generation of plane pattern intelligent design more convenient and fast, a pattern design generation method based on Reversible Jump Markov Chain Monte Carlo (rjmcmc) algorithm was proposed, and the correlation was carried out. At the same time, the same type of genetic algorithm (GA) and ant colony algorithm (AC) were introduced for performance comparison and verification. The research results show that the maximum pattern sampling accuracy of the rjmcmc algorithm is about 0.972, and the AUC value corresponding to the ROC curve is about 0.936, which are higher than other algorithms. In the algorithm iteration process, the iterative efficiency of the rjmcmc algorithm is also higher than the other two algorithms. At the same time, in the same pattern sampling processing time, the sampling number of rjmcmc algorithm is significantly higher than the other two algorithms. In the comparison of the loss value curve, the rjmcmc algorithm shows a better fitting effect, and its loss value is lower than the other two algorithms after iteration. In addition, after iteration, the error of rjmcmc algorithm is also smaller than the other two algorithms. Comprehensive analysis shows that the rjmcmc algorithm has excellent performance in pattern sampling, which can provide great help for the design and generation of plane patterns.

关键词： iterative efficiency pattern design rjmcmc algorithm sampling quantity GA algorithm AC algorithm

来源：评论

学校读者我要写书评

暂无评论

COUPLING REGULAR TESSELLATION WITH rjmcmc algorithm TO SEGMENT SAR IMAGE WITH UNKNOWN NUMBER OF CLASSES 23

COUPLING REGULAR TESSELLATION WITH RJMCMC ALGORITHM TO SEGME...

引用

23rd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS)

作者： Wang, Y. Li, Y. Zhao, Q. H. Liaoning Tech Univ Sch Geomat Inst Remote Sensing Sci & Applicat Fuxin 123000 Liaoning Peoples R China

This paper presents a Synthetic Aperture Radar (SAR) image segmentation approach with unknown number of classes, which is based on regular tessellation and Reversible Jump Markov Chain Monte Carlo (rjmcmc') algorithm. First of all, an image domain is portioned into a set of blocks by regular tessellation. The image is modeled on the assumption that intensities of its pixels in each homogeneous region satisfy an identical and independent Gamma distribution. By Bayesian paradigm, the posterior distribution is obtained to build the region-based image segmentation model. Then, a rjmcmc algorithm is designed to simulate from the segmentation model to determine the number of homogeneous regions and segment the image. In order to further improve the segmentation accuracy, a refined operation is performed. To illustrate the feasibility and effectiveness of the proposed approach, two real SAR image is tested.

关键词： SAR Image Segmentation Segmentation with Unknown Number of Classes Regular Tessellation rjmcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

Bayesian Analysis of Mixture Structural Equation Models With an Unknown Number of Components

引用

STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 2018年第1期25卷 41-55页

作者： Liu, Hefei Song, Xin Yuan Qujing Normal Univ Qujing Peoples R China Chinese Univ Hong Kong Shatin Hong Kong Peoples R China

Multivariate heterogenous data with latent variables are common in many fields such as biological, medical, behavioral, and social-psychological sciences. Mixture structural equation models are multivariate techniques used to examine heterogeneous interrelationships among latent variables. In the analysis of mixture models, determination of the number of mixture components is always an important and challenging issue. This article aims to develop a full Bayesian approach with the use of reversible jump Markov chain Monte Carlo method to analyze mixture structural equation models with an unknown number of components. The proposed procedure can simultaneously and efficiently select the number of mixture components and conduct parameter estimation. Simulation studies show the satisfactory empirical performance of the method. The proposed method is applied to study risk factors of osteoporotic fractures in older people.

关键词： Bayesian method latent variables mixture models rjmcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

Flexible modelling of random effects in linear mixed models - A Bayesian approach

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2008年第3期52卷 1347-1361页

作者： Ho, Remus K. W. Hu, Inchi Chinese Univ Hong Kong Dept Stat Shatin Hong Kong Peoples R China Hong Kong Univ Sci & Technol Dept Informat & Syst Management Hong Kong Hong Kong Peoples R China

Flexible modelling of random effects in linear mixed models has attracted some attention recently. In this paper, we propose the use of finite Gaussian mixtures as in Verbeke and Lesaffre [A linear mixed model with heterogeneity in the random-effects population, J. Amu. Statist. Assoc. 91, 217-221]. We adopt a fully Bayesian hierarchical framework that allows simultaneous estimation of the number of mixture components together with other model parameters. The technique employed is the Reversible Jump MCMC algorithm (Richardson and Green [On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion). J. Roy. Statist. Soc. Ser.,B 59, 731-792]). This approach has the advantage of producing a direct comparison of different mixture models through posterior probabilities from a single run of the MCMC algorithm. Moreover, the Bayesian setting allows us to integrate over different mixture models to obtain a more robust density estimate of the random effects. We focus on linear mixed models with a random intercept and a random slope. Numerical results on simulated data sets and a real data set are provided to demonstrate the usefulness of the proposed method. (c) 2007 Elsevier B.V All rights reserved.

关键词： Bayesian hierarchical model linear mixed model mixture models random effects rjmcmc algorithm

来源：评论

学校读者我要写书评

暂无评论

A Reversible Jump MCMC in Bayesian Blind Deconvolution With a Spherical Prior

引用

IEEE SIGNAL PROCESSING LETTERS 2022年 29卷 2372-2376页

作者： Traulle, Benjamin Bidon, Stephanie Roque, Damien Univ Toulouse ISAE SUPAERO F-31055 Toulouse France

Blind deconvolution (BD) is the recovery of a scene of interest convolved with an unknown impulse response function. Within a Bayesian context, prior distributions are assigned to both unknowns to regularize the BD problem. A common approach is to use Gaussian priors. Nonetheless, the latter do not alleviate the so-called scale ambiguity that prevents from efficiently sampling the joint posterior distribution and building appropriate estimators. In this paper, we instead use a Von-Mises prior that removes scale ambiguity and we focus on the design of a sampling scheme of the joint posterior. The latter may exhibit multiple modes. We propose accordingly a reversible jump Markov chain Monte Carlo method that prevents samples from lingering in local modes. Compared to state-of-the-art techniques, the algorithm shows an improved within- and between-mode mixing property with synthetic data. This paves the way for the design of Bayesian estimators naturally deprived of scale ambiguity.

关键词： Bayes methods Signal processing algorithms Deconvolution Space exploration Monte Carlo methods Markov processes Convolution Blind deconvolution multimodal posteriors rjmcmc algorithm scale ambiguity von Mises-Fisher prior

来源：评论

学校读者我要写书评

暂无评论

Bayesian analysis of hidden Markov structural equation models with an unknown number of hidden states

引用

ECONOMETRICS AND STATISTICS 2021年 18卷 29-43页

作者： Liu, Hefei Song, Xinyuan Capital Univ Econ & Business Sch Stat Beijing Peoples R China Chinese Univ Hong Kong Dept Stat Hong Kong Peoples R China

Hidden Markov models (HMMs) are widely used to analyze heterogeneous longitudinal data owing to their capability to model dynamic heterogeneity. Early advancements in HMMs have mainly assumed that the number of hidden states is fixed and predetermined based on the knowledge of the subjects or a certain criterion. However, as a limitation, this approach determines the number of hidden states on a pairwise basis, which becomes increasingly tedious when the state space is enlarged. Moreover, criterion-based statistics tend to select complex models with overestimated numbers of components in mixture modeling. A full Bayesian approach is developed to analyze hidden Markov structural equation models with an unknown number of hidden states. An efficient and hybrid algorithm that combines the reversible jump Markov chain Monte Carlo (rjmcmc) algorithm, the forward filtering and backward sampling scheme, and the Metropolis-Hastings algorithm is proposed to simultaneously select the number of hidden states and perform parameter estimation. The simulation study shows the satisfactory performance of the proposed method. Two real datasets collected from the UCLA Drug Abuse Research Center and National Longitudinal Survey of Youth are analyzed. (C) 2020 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

关键词： Hidden Markov model Latent variables Multivariate longitudinal data rjmcmc algorithm Structural equation model

来源：评论

学校读者我要写书评

暂无评论

An Energy-Based SAR Image Segmentation Method with Weighted Feature

引用

REMOTE SENSING 2019年第10期11卷 1169-1169页

作者： Wang, Yu Zhou, Guoqing You, Haotian Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China

To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weighted features. To precisely segment a SAR image, multiple structural features are incorporated into a block- and energy-based segmentation model in weighted way. In this paper, the multiple features of a pixel, involving spectral feature obtained from original SAR image, texture and boundary features extracted by a curvelet transform, form a feature vector. All the pixels' feature vectors form a feature set of a SAR image. To automatically determine the roles of the multiple features in the segmentation procedure, weight variables are assigned to them. All the weight variables form a weight set. Then the image domain is partitioned into a set of blocks by regular tessellation. Afterwards, an energy function and a non-constrained Gibbs probability distribution are used to combine the feature and weight sets to build a block-based energy segmentation model with feature weighted on the partitioned image domain. Further, a reversible jump Markov Chain Monte Carlo (rjmcmc) algorithm is designed to simulate from the segmentation model. In the rjmcmc algorithm, three move types were designed according to the segmentation model. Finally, the proposed method was tested on the SAR images, and the quantitative and qualitative results demonstrated its effectiveness.

关键词： SAR image segmentation with weighted feature energy function curvelet feature rjmcmc algorithm

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：