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Facial Landmark Localization by gibbs sampling

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ZTE Communications 2014年第4期12卷 23-29页

作者： Bofei Wang Diankai Zhang Chi Zhang Jiani Hu Weihong Deng ZTE Corporation Beijing University of Posts and Telecommunication

In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP crite rion. Conventional gradient ascent algorithms get stuck at the local optimal solution. gibbs sampling is a kind of Markov Chain Monte Carlo （MCMC） algorithm. We choose it for optimization because it is easy to implement and it guarantees global conver gence. The posterior distribution is obtained by learning prior distribution and likelihood function. Prior distribution is assumed Gaussian. We use Principle Component Analysis （PCA） to reduce the dimensionality and learn the prior distribution. Local Linear Support Vector Machine （LLSVM） is used to get the likelihood function of every key point. In our experiment, we compare our de tector with some other wellknown methods. The results show that the proposed method is very simple and efficient. It can avoid trapping in local optimal solution.

关键词： facial landmarks MAP gibbs sampling MCMC LL-SVM

基于gibbs sampling和XGBoost的个人贷款违约预测研究

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基于Gibbs Sampling和XGBoost的个人贷款违约预测研究

作者：张高慧郑州大学

学位级别：硕士

目前对于个人信贷科学精准审批是各大银行以及相关金融机构的主要关注点,这关系到最后的贷款收回情况。所以也必定要求各大金融贷款机构能够获得一套关于个人贷款违约情况的可靠的指标体系,建立一套科学准确的,并能够挖掘用户潜在信息,... 详细信息

目前对于个人信贷科学精准审批是各大银行以及相关金融机构的主要关注点,这关系到最后的贷款收回情况。所以也必定要求各大金融贷款机构能够获得一套关于个人贷款违约情况的可靠的指标体系,建立一套科学准确的,并能够挖掘用户潜在信息,自动识别用户借贷行为的贷款风险预测模型体系。经典可靠的贷款违约预测模型有基于XGBoost的分类模型,该模型的特征筛选方法一般都会使用特征工程与IV值(特征变量的预测能力)方法进行特征指标筛选,但本身IV值的计算方法属于线性计算方法,而XGBoost是非线性模型,以IV值为标准进行筛选得到的变量与XGBoost模型并不切合。本文将采用在MCMC框架下使用gibbs sampling方法来对影响个人贷款情况的特征进行筛选提取,以XGBoost作为筛选工具,对影响个人贷款的关联特征因子进行随机搜索提取,筛选得到的变量在预期中更符合XGBoost模型的搭建。与传统的特征筛选方法进行,分析两种特征体系的异同之处,并基于两种指标体系进行经典的XGBoost模型搭建,发现基于gibbs sampling方法筛选出的的特征体系进入XGBoost模型后,模型的各项性能指标优于以IV值筛选特征为基础的XGBoost。并且在搭建过程中发现,机器学习模型不具有可解释性,所以加入SHAP解释方法,对各个指标是如何影响XGBoost模型的分类预测结果进行详细的解释。

关键词：机器学习个人贷款 XGBoost gibbs sampling SHAP

基于gibbs sampling算法的转录因子结合位点预测研究与实现

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基于Gibbs Sampling算法的转录因子结合位点预测研究与实现

作者：李炳利吉林大学

学位级别：硕士

本文主要研究基于马尔可夫链－蒙特卡罗（MCMC）理论的转录因子结合位点预测算法及其相关软件的应用。具体内容包括：研究MCMC理论及其中重要算法gibbs sampling的具体理论与实现。在Linux操作系统下通过使用Qt（一个非常优秀的基于C++... 详细信息

本文主要研究基于马尔可夫链－蒙特卡罗（MCMC）理论的转录因子结合位点预测算法及其相关软件的应用。具体内容包括：研究MCMC理论及其中重要算法gibbs sampling的具体理论与实现。在Linux操作系统下通过使用Qt（一个非常优秀的基于C++语言的跨平台的图形用户界面开发工具）对转录因子结合位点预测工具包——BEST进行应用及分析。本文首先就马尔可夫链－蒙特卡罗理论的发展现状作简要阐述，提出本课题研究的意义。然后对预测共同的转录因子结合位点的数学模型及模型采用的算法进行概述，在其中详细介绍了gibbs sampling算法。接下来介绍了结合位点预测工具包的使用。最后,对基于gibbssampling的生命探索者软件的基本原理及实现进行讲解，包括：基本原理，使用的数学模型和算法及具体的应用。提出了生命探索者软件的改进方向，考虑结合神经网络等智能算法，进一步改善预测软件的预测准确性，并提高软件的适应性。

关键词：随机过程马尔可夫链-蒙特卡罗 gibbs sampling 数学模型回文

基于LightGBM-gibbs sampling的特征选择算法研究

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现代计算机 2022年第6期28卷 56-59页

作者：产胜宁贵州财经大学贵阳550025

针对数据维度较高、数据的冗余性导致模型训练带来的复杂性和挖掘不足的问题,创新性地提出了一种基于轻量梯度提升机(light gradient boosting machine,LightGBM)算法的吉布斯采样(gibbs sampling)方法,获取近似于目标分布的样本数据集... 详细信息

针对数据维度较高、数据的冗余性导致模型训练带来的复杂性和挖掘不足的问题,创新性地提出了一种基于轻量梯度提升机(light gradient boosting machine,LightGBM)算法的吉布斯采样(gibbs sampling)方法,获取近似于目标分布的样本数据集,对样本数据集进行分析得到每个特征的重要度,选取一定的特征实现特征选择,实现降维。为验证这种方法的有效性,选取一些典型的特征选择算法进行对比,通过交叉验证的方法,与之进行比较,发现基于gibbs sampling的特征选择算法会取得更好的准确率,与原始数据集相比,能有效降低特征,同时提高了模型预测能力。

关键词： LightGBM gibbs sampling 特征选择交叉验证

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基于吉布斯采样的稀疏水声信道估计方法

哈尔滨工程大学学报（英文版） 2024年第2期23卷 434-442页

作者：佟文涛葛威贾亦真张嘉恒 National Key Laboratory of Underwater Acoustic Technology Harbin Engineering UniversityHarbin 150001China Key Laboratory for Polar Acoustics and Application of Ministry of Education Harbin Engineering UniversityHarbin 150001China College of Underwater Acoustic Engineering Harbin Engineering UniversityHarbin 150001China Qingdao Innovation and Development Center of Harbin Engineering University Qingdao266400China State Key Laboratory of Acoustics Institute of AcousticsChinese Academy of SciencesBeijing 100190China

The estimation of sparse underwater acoustic(UWA)channels can be regarded as an inference problem involving hidden variables within the Bayesian *** the classical sparse Bayesian learning(SBL),derived through the expectation maximization(EM)algorithm,has been widely employed for UWA channel estimation,it still differs from the real posterior expectation of *** this paper,we propose an approach that combines variational inference(VI)and Markov chain Monte Carlo(MCMC)methods to provide a more accurate posterior ***,the SBL is first re-derived with VI,allowing us to replace the posterior distribution of the hidden variables with a variational ***,we determine the full conditional probability distribution for each variable in the variational distribution and then iteratively perform random gibbs sampling in MCMC to converge the Markov *** results of simulation and experiment indicate that our estimation method achieves lower mean square error and bit error rate compared to the classic SBL ***,it demonstrates an acceptable convergence speed.

关键词： Sparse bayesian learning Channel estimation Variational inference gibbs sampling

Monte-Carlo based random passive energy beamforming for reconfigurable intelligent surface assisted wireless power transfer

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Digital Communications and Networks 2023年第3期9卷 667-676页

作者： Ziyang Lu Yubin Zhao Xiaofan Li School of Microelectronics Science and Technology Sun Yat-Sen UniversityZhuhai519082China School of Intelligent System Science and Engineering Jinan UniversityZhuhai519070China

Reconfigurable intelligent surface(RIS)employs passive beamforming to control the wireless propagation channel,which benefits the wireless communication capacity and the received energy efficiency of wireless power transfer(WPT)*** beamforming schemes are classified as discrete and non-convex integer program-ming *** this paper,we propose a Monte-Carlo(MC)based random energy passive beamforming of RIS to achieve the maximum received power of electromagnetic(EM)WPT ***,the gibbs sampling and re-sampling methods are employed to generate phase shift vector *** the sample with the maximum received power is considered the optimal *** order to adapt to the application scenarios,we develop two types of passive beamforming algorithms based on such MC sampling *** first passive beamforming uses an approximation of the integer programming as the initial sample,which is calculated based on the channel *** the second one is a purely randomized algorithm with the only total received power *** proposed methods present several advantages for RIS control,e.g.,fast convergence,easy implementation,robustness to the channel noise,and limited feedback requirement,and they are applicable even if the channel information is *** to the simulation results,our proposed methods outperform other approxi-mation and genetic *** our methods,the WPT system even significantly improves the power effi-ciency in the nonline-of-sight(NLOS)environment.

关键词： Reconfigurable intelligence surface Wireless power transfer Monte-carlo algorithm Passive beamforming gibbs sampling

Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks

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Science China(Information Sciences) 2018年第2期61卷 100-114页

作者： Wenxiang ZHU Pingping XU Thi Oanh BUI Guilu WU Yan YANG National Mobile Communications Research Laboratory Southeast University Department of Electronic and Electrical Engineering Bengbu University

In recent years, energy efficiency has become an important topic, especially in the field of ultradense networks(UDNs). In this area, cell-association bias adjustment and small cell on/off are proposed to enhance the performance of energy efficiency in UDNs. This is done by changing the cell association relationship and turning off the extra small cells that have no users. However, the variety of cell association relationships and the switching on/off of the small cells may deteriorate some users’ data rates, leading to nonconformance to the users’ data rate requirement. Considering the discreteness and non-convexity of the energy efficiency optimization problem and the coupled relationship between cell association and scheduling during the optimization process, it is difficult to achieve an optimal cell-association bias. In this study, we optimize the network energy efficiency by adjusting the cell-association bias of small cells while satisfying the users’ data rate requirement. We propose an energy-efficient centralized gibbs sampling based cell-association bias adjustment(CGSCA) algorithm. In CGSCA, global information such as channel state information, cell association information, and network load information need to be collected. Then, considering the overhead of the messages that are exchanged and the implementation complexity of CGSCA to obtain the global information in UDNs, we propose an energy-efficient distributed gibbs sampling based cell-association bias adjustment(DGSCA) algorithm with a lower message-exchange overhead and implementation *** DGSCA, we derive the updated formulas for calculating the number of users in a cell and the users’ SINR. We analyze the implementation complexities(e.g., computation complexity and communication complexity) of the proposed two algorithms and other existing algorithms. We perform simulations, and the results show that CGSCA and DGSCA have faster convergence speed, as well as a higher performance gain o

关键词： ultra-dense networks cell-association bias energy efficiency gibbs sampling users’ data rate constraint

Bayesian Planning of Optimal Step-stress Accelerated Life Test for Log-location-scale Distributions

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Acta Mathematicae Applicatae Sinica 2018年第1期34卷 51-64页

作者： Qiang GUAN Yin-cai TANG Institute of Information Engineering Sanming University Sanming 365004 China School of Finance and Statistics East China Normal University Shanghai 200241 China

This paper introduces some Bayesian optimal design methods for step-stress accelerated life test planning with one accelerating variable, when the acceleration model is linear in the accelerated variable or its function, based on censored data from a log-location-scale distributions. In order to find the optimal plan,we propose different Monte Carlo simulation algorithms for different Bayesian optimal criteria. We present an example using the lognormal life distribution with Type-I censoring to illustrate the different Bayesian methods and to examine the effects of the prior distribution and sample size. By comparing the different Bayesian methods we suggest that when the data have large（small） sample size B1（τ）（B2（τ）） method is adopted. Finally, the Bayesian optimal plans are compared with the plan obtained by maximum likelihood method.

关键词： accelerated life testing Bayesian approach gibbs sampling type-I censoring log-location-scale distributions optimal design.

Low-complexity single-channel blind source separation

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The Journal of China Universities of Posts and Telecommunications 2017年第3期24卷 7-15页

作者： Zhang Xing Hu Jianhao National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China

For the time-frequency overlapped signals, a low-complexity single-channel blind source separation （SBSS） algorithm is proposed in this paper. The algorithm does not only introduce the gibbs sampling theory to separate the mixed signals, but also adopts the orthogonal triangle decomposition-M （QRD-M） to reduce the computational complexity. According to analysis and simulation results, we demonstrate that the separation performance of the proposed algorithm is similar to that of the per-survivor processing （PSP） algorithm, while its computational complexity is sharply reduced.

关键词： single-channel separation gibbs sampling QRD-M