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Continuous Bayesian probability estimator in predictions of nuclear charge radii 认领 引用 被引量：2
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作者 Jian Liu Kai-Zhong Tan +4 位作者 Lei Wang Wan-Qing Gao Tian-Shuai Shang Jian Li Chang Xu 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2025年第11期283-293,共11页
Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator ... Recently,machine learning has become a powerful tool for predicting nuclear charge radius RC,providing novel insights into complex physical phenomena.This study employs a continuous Bayesian probability(CBP)estimator and Bayesian model averaging(BMA)to optimize the predictions of RCfrom sophisticated theoretical models.The CBP estimator treats the residual between the theoretical and experimental values of RCas a continuous variable and derives its posterior probability density function(PDF)from Bayesian theory.The BMA method assigns weights to models based on their predictive performance for benchmark nuclei,thereby accounting for the unique strengths of each model.In global optimization,the CBP estimator improved the predictive accuracy of the three theoretical models by approximately 60%.The extrapolation analyses consistently achieved an improvement rate of approximately 45%,demonstrating the robustness of the CBP estimator.Furthermore,the combination of the CBP and BMA methods reduces the standard deviation to below 0.02 fm,effectively reproducing the pronounced shell effects on RCof the Ca and Sr isotope chains.The studies in this paper propose an efficient method to accurately describe RCof unknown nuclei,with potential applications in research on other nuclear properties. 展开更多
关键词 Machine learning Nuclear charge radii Continuous Bayesian probability estimator Bayesian model averaging
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Reducing variance of measurement in optical sensing based on self-Bayesian estimation 认领 引用
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作者 Xuezhi Zhang Shengliang Zhang +5 位作者 Junfeng Jiang Kun Liu Jiahang Jin Wenxin Bo Ruofan Wang Tiegen Liu 《Advanced Photonics Nexus》 CSCD 2025年第2期64-74,共11页
In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,wh... In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,which is expected to help further enhance sensing precision.We propose a self-Bayesian estimate of the method,aiming at reducing measurement variance.This method utilizes the intensity and phase of the parameter to be measured,achieving statistical optimization of the estimated value through Bayesian inference,effectively reducing the measurement variance.To demonstrate the effectiveness of this method,we adopted an optical fiber heterodyne interference sensing vibration measurement system.The experimental results show that the signal-to-noise ratio is effectively improved within the frequency range of 200 to 500 kHz.Moreover,it is believed that the self-Bayesian estimation method holds broad application prospects in various types of optical sensing. 展开更多
关键词 optical sensing Bayesian estimation signal processing
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Bayesian phase difference estimation based on single-photon projective measurement 认领 引用
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作者 Xu-Hao Yu Ying Wei +7 位作者 Ran Yang Wen-Hui Song Yingning Miao Wei Zhou Xinhui Li Xiaoqin Gao Yan-Xiao Gong Shi-Ning Zhu 《Chinese Physics B》 SCIE EI CAS CSCD 2025年第7期89-93,共5页
The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale ... The estimation of quantum phase differences plays an important role in quantum simulation and quantum computation,yet existing quantum phase estimation algorithms face critical limitations in noisy intermediate-scale quantum(NISQ)devices due to their excessive depth and circuit complexity.We demonstrate a high-precision phase difference estimation protocol based on the Bayesian phase difference estimation algorithm and single-photon projective measurement.The iterative framework of the algorithm,combined with the independence from controlled unitary operations,inherently mitigates circuit depth and complexity limitations.Through an experimental realization on the photonic system,we demonstrate high-precision estimation of diverse phase differences,showing root-mean-square errors(RMSE)below the standard quantum limit(1/√N)and reaching the Heisenberg scaling(1/N)after a certain number of iterations.Our scheme provides a critical advantage in quantum resource-constrained scenarios,and advances practical implementations of quantum information tasks under realistic hardware constraints. 展开更多
关键词 Bayesian phase difference estimation single-photon projection measurement Heisenberg limit quantum information quantum state engineering and measurements
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TopoEKF:From State-Space Estimation to Topological Signatures for Enhanced Multi-Object Tracking and Anomaly Detection in UAVs 认领 引用
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作者 Rabia Kıratlı Hatice Unlu Eroğlu Alperen Eroğlu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期860-908,共49页
Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions.This study presents a mathematical... Reliable multi-object detection and tracking play a critical role in Unmanned Aerial Vehicles-based aerial surveillance applications operating under challenging real-world conditions.This study presents a mathematically grounded,model-driven tracking framework named TopoEKF,which integrates an enhanced Adaptive Extended Kalman Filter with Topological Data Analysis to improve both tracking robustness and anomaly detection performance.Unlike prior approaches that primarily focus on refining object detection architectures,this work emphasizes the predictive power of iterative Bayesian filtering,optimal state estimation,and adaptive error minimization within a unified mathematical framework.The proposed system employs a carefully optimized YOLOvl2 detector to provide accurate object location priors,followed by a formally defined discrete-time linear Gaussian tracking model.The Adaptive EKF is leveraged to handle nonlinearities arising from the projection of three-dimensional object motion onto the two-dimensional image plane through local linearization.To further enhance robustness under low resolution,large object-to-image distances,frequent occlusions,and environmental noise,TopoEKF introduces adaptive noise covariance modeling driven by measurement confidence,occlusion status,and topological feedback.Persistent homology is applied to EKF-filtered trajectories to extract topological signatures that characterize the global structure of object motion.These features are transformed into fixed-dimensional representations and processed by an unsupervised Isolation Forest classifier for trajectory-level anomaly detection.Experimental evaluations are conducted on a challenging hybrid dataset combining scenarios from COCO,VisDrone,UAVDT,Road_Anomaly_Dataset,and DoTA benchmarks.Quantitative results demonstrate that TopoEKF improves Multi-Object Tracking Accuracy from 72.8%to 76.3%and reduces identity switches by approximately 34%compared to a standard EKF baseline.The enhanced EKF achieves up to 20%higher robustness in highly noisy and indoor environments while maintaining realtime performance at 28.5 frames per second on resource-constrained embedded platforms.In the anomaly detection stage,the integration of persistent homology-based features improves the F1-score from 66%to 84%,with substantial gains in both precision and recall.Overall,the proposed approach highlights the effectiveness of interpretable,mathematically founded state estimation models as a reliable and efficient alternative to black-box deep learning systems in safety-critical UAV applications. 展开更多
关键词 Extended Kalman filter UAV tracking multi-object tracking mathematical modeling Bayesian estimation YOLOvl2 topological data analysis persistent homology
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Bayesian seismic multi-scale inversion in complex Laplace mixed domains 认领 引用 被引量：11
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作者 Kun Li Xing-Yao Yin Zhao-Yun Zong 《Petroleum Science》 SCIE CAS CSCD 2017年第4期694-710,共17页
Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency respo... Seismic inversion performed in the time or frequency domain cannot always recover the long-wavelength background of subsurface parameters due to the lack of low-frequency seismic records. Since the low-frequency response becomes much richer in the Laplace mixed domains, one novel Bayesian impedance inversion approach in the complex Laplace mixed domains is established in this study to solve the model dependency problem. The derivation of a Laplace mixed-domain formula of the Robinson convolution is the first step in our work. With this formula, the Laplace seismic spectrum, the wavelet spectrum and time-domain reflectivity are joined together. Next, to improve inversion stability, the object inversion function accompanied by the initial constraint of the linear increment model is launched under a Bayesian framework. The likelihood function and prior probability distribution can be combined together by Bayesian formula to calculate the posterior probability distribution of subsurface parameters. By achieving the optimal solution corresponding to maximum posterior probability distribution, the low-frequency background of subsurface parameters can be obtained successfully. Then, with the regularization constraint of estimated low frequency in the Laplace mixed domains, multi-scale Bayesian inversion inthe pure frequency domain is exploited to obtain the absolute model parameters. The effectiveness, anti-noise capability and lateral continuity of Laplace mixed-domain inversion are illustrated by synthetic tests. Furthermore,one field case in the east of China is discussed carefully with different input frequency components and different inversion algorithms. This provides adequate proof to illustrate the reliability improvement in low-frequency estimation and resolution enhancement of subsurface parameters, in comparison with conventional Bayesian inversion in the frequency domain. 展开更多
关键词 Low-frequency Complex mixed-domain Laplace inversion Bayesian estimation Multi-scale inversion
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Target threat estimation based on discrete dynamic Bayesian networks with small samples 认领 引用 被引量：7
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作者 YE Fang MAO Ying +1 位作者 LI Yibing LIU Xinrui 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第5期1135-1142,共8页
The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target thr... The accuracy of target threat estimation has a great impact on command decision-making.The Bayesian network,as an effective way to deal with the problem of uncertainty,can be used to track the change of the target threat level.Unfortunately,the traditional discrete dynamic Bayesian network(DDBN)has the problems of poor parameter learning and poor reasoning accuracy in a small sample environment with partial prior information missing.Considering the finiteness and discreteness of DDBN parameters,a fuzzy k-nearest neighbor(KNN)algorithm based on correlation of feature quantities(CF-FKNN)is proposed for DDBN parameter learning.Firstly,the correlation between feature quantities is calculated,and then the KNN algorithm with fuzzy weight is introduced to fill the missing data.On this basis,a reasonable DDBN structure is constructed by using expert experience to complete DDBN parameter learning and reasoning.Simulation results show that the CF-FKNN algorithm can accurately fill in the data when the samples are seriously missing,and improve the effect of DDBN parameter learning in the case of serious sample missing.With the proposed method,the final target threat assessment results are reasonable,which meets the needs of engineering applications. 展开更多
关键词 discrete dynamic Bayesian network(DDBN) parameter learning missing data filling Bayesian estimation
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Modified unscented particle filter for nonlinear Bayesian tracking 认领 引用 被引量：14
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作者 Zhan Ronghui Xin Qin Wan Jianwei 《Journal of Systems Engineering and Electronics》 SCIE EI 2008年第1期7-14,共8页
A modified unscented particle filtering scheme for nonlinear tracking is proposed,in view of the potential drawbacks(such as,particle impoverishment and numerical sensitivity in calculating the prior)of the convention... A modified unscented particle filtering scheme for nonlinear tracking is proposed,in view of the potential drawbacks(such as,particle impoverishment and numerical sensitivity in calculating the prior)of the conventional unscented particle filter(UPF)confronted in practice.Specifically,a different derivation of the importance weight is presented in detail.The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution,Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one. 展开更多
关键词 Bayesian estimation modified unscented particle filter nonlinear filtering unscented Kalman filter
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SAR Images Despeckling Based on Bayesian Estimation and Fuzzy Shrinkage in Wavelet Domains 认领 引用 被引量：3
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作者 吴艳 王霞 廖桂生 《Chinese Journal of Aeronautics》 EI CAS 2006年第4期326-333,共8页
An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance i... An efficient despeclding algorithm is proposed based on stationary wavelet transform （SWT） for synthetic aperture radar （SAR） images. The statistical model of wavelet coefficients is analyzed and its performance is modeled with a mixture density of two zero-mean Gaussian distributions. A fuzzy shrinkage factor is derived based on the minimum mean square error （MMSE） criteria with Bayesian estimation. In the case above, the ideas of region division and fuzzy shrinkage arc adopted according to the interscale dependencies among wavelet coefficients. The noise-free wavelet coefficients are estimated accurately. Experimental results show that the algorithm proposed is superior to the refined Lee filter, wavelet soft thresbolding shrinkage and SWT shrinkage algorithms in terms of smoothing effects and edges preservation. 展开更多
关键词 SAR image despeclding fuzzy shrinkage factor MMSE region division. Bayesian estimation SWT
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Single channel signal component separation using Bayesian estimation 认领 引用 被引量：4
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作者 Cai Quanwei Wei Ping Xiao Xianci 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第1期33-39,共7页
A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper.By using the basis function projection,the component separation becomes a problem of limited p... A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper.By using the basis function projection,the component separation becomes a problem of limited parameter estimation.Then,a Bayesian model for estimating parameters is set up.The reversible jump MCMC(Monte Carlo Markov Chain)algorithmis adopted to perform the Bayesian computation.The method can jointly estimate the parameters of each component and the component number.Simulation results demonstrate that the method has low SNR threshold and better performance. 展开更多
关键词 Signal component separation Single channel Bayesian estimation Reversible jump MCMC
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Recent advances in simulation-based inference for gravitational wave data analysis 认领 引用
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作者 Bo Liang He Wang 《Astronomical Techniques and Instruments》 CSCD 2026年第2期93-111,共19页
The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level anal... The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level analyses.Traditional Bayesian inference methods,particularly Markov chain Monte Carlo,face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data.This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy,with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation.We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods,including neural posterior estimation,neural ratio estimation,neural likelihood estimation,flow matching,and consistency models.We explore the applications of these methods across diverse gravitational wave data processing scenarios,from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies.Although these techniques demonstrate speed improvements over traditional methods in controlled studies,their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption.Their accuracy,which is similar to that of conventional methods,requires further validation across broader parameter spaces and noise conditions. 展开更多
关键词 Simulation-based inference Gravitational wave astronomy Normalizing flows Neural posterior estimation Bayesian parameter estimation
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Bayesian Estimation and Hierarchical Bayesian Estimation of Zero-failure Data 认领 引用 被引量：11
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作者 韩明 《Chinese Quarterly Journal of Mathematics》 2001年第1期65-70,共6页
The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at... The zero_failure data research is a new field in the recent years, but it is required urgently in practical projects, so the work has more theory and practical values. In this paper, for zero_failure data (t i,n i) at moment t i , if the prior distribution of the failure probability p i=p{T【t i} is quasi_exponential distribution, the author gives the p i Bayesian estimation and hierarchical Bayesian estimation and the reliability under zero_failure date condition is also obtained. 展开更多
关键词 reliability zero_failure data failure probability Bayesian estimation hierarchical Bayesian estimaiton
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Dynamic Bayesian estimation of displacement parameters of continuous curve box based on Novozhilov theory 认领 引用
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作者 张剑 叶见曙 赵新铭 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2007年第1期87-95,共9页
The finite strip controlling equation of pinned curve box was deduced on basis of Novozhilov theory and with flexibility method, and the problem of continuous curve box was resolved. Dynamic Bayesian error function of... The finite strip controlling equation of pinned curve box was deduced on basis of Novozhilov theory and with flexibility method, and the problem of continuous curve box was resolved. Dynamic Bayesian error function of displacement parameters of continuous curve box was found. The corresponding formulas of dynamic Bayesian expectation and variance were derived. After the method of solving the automatic search of step length was put forward, the optimization estimation computing formulas were also obtained by adapting conjugate gradient method. Then the steps of dynamic Bayesian estimation were given in detail. Through analysis of a Classic example, the criterion of judging the precision of the known information is gained as well as some other important conclusions about dynamic Bayesian stochastic estimation of displacement parameters of continuous curve box. 展开更多
关键词 displacement parameters Bayesian estimation Novozhilov theory continuous curve box
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Bayesian estimation for nonlinear stochastic hybrid systems with state dependent transitions 认领 引用
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作者 Shunyi Zhao Fei Liu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2012年第2期242-249,共8页
The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov ... The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few liter- ature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive as- sumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example . 展开更多
关键词 Bayesian estimation nonlinear stochastic hybrid sys- tem state dependent transition cell space.
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Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data 认领 引用 被引量：1
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作者 Naif Alotaibi A.S.Al-Moisheer +2 位作者 Ibrahim Elbatal Mohammed Elgarhy Ehab M.Almetwally 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2795-2823,共29页
This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation ... This article introduces a novel variant of the generalized linear exponential(GLE)distribution,known as the sine generalized linear exponential(SGLE)distribution.The SGLE distribution utilizes the sine transformation to enhance its capabilities.The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues.The suggested model incorporates a hazard rate function(HRF)that may display a rising,J-shaped,or bathtub form,depending on its unique characteristics.This model includes many well-known lifespan distributions as separate sub-models.The suggested model is accompanied with a range of statistical features.The model parameters are examined using the techniques of maximum likelihood and Bayesian estimation using progressively censored data.In order to evaluate the effectiveness of these techniques,we provide a set of simulated data for testing purposes.The relevance of the newly presented model is shown via two real-world dataset applications,highlighting its superiority over other respected similar models. 展开更多
关键词 Sine G family generalized linear failure rate progressively censored data moments maximum likelihood estimation Bayesian estimation simulation
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Entropy Bayesian Analysis for the Generalized Inverse Exponential Distribution Based on URRSS 认领 引用 被引量：1
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作者 Amer I.Al-Omari Amal S.Hassan +2 位作者 Heba F.Nagy Ayed R.A.Al-Anzi Loai Alzoubi 《Computers, Materials & Continua》 SCIE EI 2021年第12期3795-3811,共17页
This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution.Assuming that the observed samples are taken from the upper record ranked set sampling(URRSS)and up... This paper deals with the Bayesian estimation of Shannon entropy for the generalized inverse exponential distribution.Assuming that the observed samples are taken from the upper record ranked set sampling(URRSS)and upper record values(URV)schemes.Formulas of Bayesian estimators are derived depending on a gamma prior distribution considering the squared error,linear exponential and precautionary loss functions,in addition,we obtain Bayesian credible intervals.The random-walk Metropolis-Hastings algorithm is handled to generate Markov chain Monte Carlo samples from the posterior distribution.Then,the behavior of the estimates is examined at various record values.The output of the study shows that the entropy Bayesian estimates under URRSS are more convenient than the other estimates under URV in the majority of the situations.Also,the entropy Bayesian estimates perform well as the number of records increases.The obtained results validate the usefulness and efficiency of the URV method.Real data is analyzed for more clarifying purposes which validate the theoretical results. 展开更多
关键词 Shannon entropy generalized inverse exponential distribution Bayesian estimators loss function ranked set sampling markov chain
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Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework 认领 引用 被引量：5
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作者 SHEN Zheqi ZHANG Xiangming TANG Youmin 《Acta Oceanologica Sinica》 SCIE CAS CSCD 2016年第3期69-78,共10页
Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods： ensemble adjustme... Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods： ensemble adjustment Kalman filter（EAKF） and sequential importance resampling particle filter（SIR-PF）, using a well-known nonlinear and non-Gaussian model（Lorenz ＇63 model）. The EAKF, which is a deterministic scheme of the ensemble Kalman filter（En KF）, performs better than the classical（stochastic） En KF in a general framework. Comparison between the SIR-PF and the EAKF reveals that the former outperforms the latter if ensemble size is so large that can avoid the filter degeneracy, and vice versa. The impact of the probability density functions and effective ensemble sizes on assimilation performances are also explored. On the basis of comparisons between the SIR-PF and the EAKF, a mixture filter, called ensemble adjustment Kalman particle filter（EAKPF）, is proposed to combine their both merits. Similar to the ensemble Kalman particle filter, which combines the stochastic En KF and SIR-PF analysis schemes with a tuning parameter, the new mixture filter essentially provides a continuous interpolation between the EAKF and SIR-PF. The same Lorenz ＇63 model is used as a testbed, showing that the EAKPF is able to overcome filter degeneracy while maintaining the non-Gaussian nature, and performs better than the EAKF given limited ensemble size. 展开更多
关键词 data assimilation ensemble adjustment Kalman filter particle filter Bayesian estimation ensemble adjustment Kalman particle filter
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E-Bayesian estimation for competing risk model under progressively hybrid censoring 认领 引用 被引量：3
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作者 Min Wu Yimin Shi Yan Wang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2016年第4期936-944,共9页
This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censori... This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censoring scheme（PHCS）. The estimations are obtained based on Gamma conjugate prior for the parameter under squared error（SE） and Linex loss functions. The simulation results are provided for the comparison purpose and one data set is analyzed. 展开更多
关键词 Bayesian estimation expected Bayesian（E-Bayesian） estimation Gompertz distribution Type-I progressively hybrid censoring
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Stock price index analysis of four OPEC members:a Bayesian approach 认领 引用
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作者 Saman Hatamerad Hossain Asgharpur +1 位作者 Bahram Adrangi Jafar Haghighat 《Financial Innovation》 2024年第1期1107-1135,共29页
This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members.Bayesian model averaging(BMA)and regularized linear regression(RLR)are employed... This study examines the relationship between macroeconomic variables and stock price indices of four prominent OPEC oil-exporting members.Bayesian model averaging(BMA)and regularized linear regression(RLR)are employed to address uncertainties arising from different estimation models and variable selection.Jointness is utilized to determine the nature of relationships among variable pairs.The case study spans macroeconomic variables and stock prices from 1996 to 2018.BMA findings reveal a strong positive association between stock price indices and both consumer price index(CPI)and broad money growth in each analyzed OPEC country.Additionally,the study suggests a weak negative correlation between OPEC oil prices and the stock price index.RLR results align with BMA analysis,offering insights valuable for policymakers and international wealth managers. 展开更多
关键词 Equities Macroeconomics Bayesian model averaging Bayesian estimation Regularized linear regression OPEC countries
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E-Bayesian Estimation of the Products Reliability when Testing Reveals no Failure 认领 引用 被引量：2
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作者 韩明 《Chinese Quarterly Journal of Mathematics》 2009年第3期407-414,共8页
This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product ... This paper develops a new method, named E-Bayesian estimation method, to estimate the reliability parameters. The E-Bayesian estimation method of the reliability are derived for the zero-failure data from the product with Binomial distribution. Firstly, for the product reliability, the definitions of E-Bayesian estimation were given, and on the base, expressions of the E-Bayesian estimation and hierarchical Bayesian estimation of the products reliability was given. Secondly, discuss properties of the E-Bayesian estimation. Finally, the new method is applied to a real zero-failure data set, and as can be seen, it is both efficient and easy to operate. 展开更多
关键词 reliability zero-failure data E-Bayesian estimation hierarchical Bayesian estimation
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Bayesian Estimation for the Order of INAR(q)Model 认领 引用 被引量：1
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作者 MIAO GUAN-HONG WANG DE-HUI 《Communications in Mathematical Research》 CSCD 2016年第4期325-331,共7页
In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss fun... In this paper, we consider the problem of determining the order ofINAR（Q） model on the basis of the Bayesian estimation theory. The Bayesian es-timator for the order is given with respect to a squared-error loss function. The consistency of the estimator is discussed. The results of a simulation study for the estimation method are presented. 展开更多
关键词 INAR（Q） model Bayesian estimation squared-error loss function con-sistency
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