Estimation and Properties of a Time-Varying GQARCH(1,1)-M Model by Sofia Anyfantaki, Antonis Demos

“…This paper outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only 𝑂(𝑇) computational operations, where 𝑇 is the sample size. …”
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Estimating MS-BLGARCH Models Using Recursive Method by Ahmed Ghezal, Imane Zemmouri

“…The main idea is to use the maximum likelihood estimation (MLE) method and from this develop a recursive Expectation-Maximization (EM) algorithm.…”
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Flexible Dirichlet Mixture Model for Multi-modal data Clustering by Seunghyun Hong, Fatma Najar, Manar Amayri, Nizar Bouguila

“…The model learning is accomplished through the method of moments and the expectation-maximization (EM) algorithm. Empirical evaluations across diverse datasets, including unimodal and multi-modal data, demonstrate the model’s superior clustering performance. …”
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On the Estimation of k-Regimes Switching of Mixture Autoregressive Model via Weibull Distributional Random Noise by Rasaki, Olawale Olanrewaju, Anthony, Gichuhi Waititu, Nafiu, Lukman Abiodun

“…We developed and established a Weibull Mixture Autoregressive model of k-regimes via WMAR(k; p1, p2, , pk ) with Expectation-Maximization (EM) algorithm adopted as parameter estimation technique. …”
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Fréchet Random Noise for k-Regime-Switching Mixture Autoregressive Model by Rasaki, Olawale Olanrewaju, Anthony, Gichuhi Waititu, Nafiu, Lukman Abiodun

“…Fréchet Mixture Autoregressive (FMAR) model of k-regime-switching, denoted by FMAR(k; p1, p2 ,, pk ) was developed and Expectation-Maximization (EM) algorithm was used as a method of parameter estimation for the embedded coefficients of AR of k-mixing weights and lag pk. …”
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Improved and efficient EM channel estimation algorithm for MIMO-OFDM systems by XU Peng1, WANG Jin-kuan1, QI Feng2

“…For multiple-input multiple-output with orthogonal frequency division multiplexing（MIMO-OFDM） systems,the error floor（EF） phenomenon at high signal noise rate（SNR） was induced by the expectation maximum（EM） channel estimation algorithm.In addition,the data transmission efficiency was declined obviously with the increasing number of transmit antennas.According to these problems,an improved and efficient EM channel estimation algorithm was pro-posed.Firstly,an accurate and equivalent signal model was introduced to derive a modified EM algorithm,which im-proved the estimation performance at high SNR.Next,to enhance the data transmission efficiency and further the esti-mate performance of the proposed algorithm,phase orthogonal pilots sequences and joint estimation were carried out over multiple OFDM symbols respectively.Simulation results show that the proposed algorithm has better estimation performance and higher data transmission efficiency.…”
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Blind audio watermarking mechanism based on variational Bayesian learning by Xin TANG, Zhao-feng MA, Xin-xin NIU, Yi-xian YANG

“…In order to improve the performance of audio watermarking detection,a blind audio watermarking mechanism using the statistical characteristics based on MFCC features of audio frames was proposed.The spread spectrum watermarking was embedded in the DCT coefficients of audio frames.MFCC features extracted from watermarked audio frames as well as un-watermarked ones were trained to establish their Gaussian mixture models and to estimate the parameters by vatiational Bayesian learning method respectively.The watermarking was detected according to the maximum likelihood principle.The experimental results show that our method can lower the false detection rate compared with the method using EM algorithm when the audio signal was under noise and malicious attacks.Also,the experiments show that the proposed method achieves better performance in handling insufficient training data as well as getting rid of over-fitting problem.…”
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Array amplitude-phase and mutual coupling error joint correction method based on sparse Bayesian by Ding WANG, Weigang GAO, Zhidong WU

“…In the actual array direction finding system, there are often a variety of errors such as amplitude and phase, mutual coupling, which lead to serious deterioration of array direction finding performance.In order to solve the problem of array direction finding misalignment in the presence of low signal-to-noise ratio, small snapshots and multiple errors, the spatial sparsity of signals were introduced, and Bayesian sparse reconstruction technology was used to solve the passive correction and joint estimation of array signal azimuth in the presence of amplitude-phase and mutual coupling errors.The over-complete model of the received signal with error was constructed, and the posterior probability density function of the received signal was obtained.The EM algorithm was used to iteratively optimize the probability density function to solve the corresponding parameters.At the same time, the CRLB of array error and signal azimuth was derived, and by experimental simulation verifies the effectiveness of the proposed method.…”
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A Bayesian Generative Model for Surface Template Estimation by Jun Ma, Michael I. Miller, Laurent Younes

“…We develop a mode approximation EM algorithm to infer the maximum a posteriori estimation of initial momentum μ, which determines the template surface. …”
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Storage life evaluation of photodetector based on multi-parameter performance degradation and competitive failure by ZHANG Yeping, YANG Tongbo, LI Ziwei

“…Furthermore, the optimal distribution of a single performance parameter was selected based on the pseudo-life data combined with the expectation maximization(EM) algorithm, and then the competitive failure evaluation of multi-parameters was carried out by Monte-Carlo sampling method. …”
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Array amplitude-phase and mutual coupling error joint correction method based on sparse Bayesian by Ding WANG, Weigang GAO, Zhidong WU

“…In the actual array direction finding system, there are often a variety of errors such as amplitude and phase, mutual coupling, which lead to serious deterioration of array direction finding performance.In order to solve the problem of array direction finding misalignment in the presence of low signal-to-noise ratio, small snapshots and multiple errors, the spatial sparsity of signals were introduced, and Bayesian sparse reconstruction technology was used to solve the passive correction and joint estimation of array signal azimuth in the presence of amplitude-phase and mutual coupling errors.The over-complete model of the received signal with error was constructed, and the posterior probability density function of the received signal was obtained.The EM algorithm was used to iteratively optimize the probability density function to solve the corresponding parameters.At the same time, the CRLB of array error and signal azimuth was derived, and by experimental simulation verifies the effectiveness of the proposed method.…”
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A Regularized Vector Autoregressive Hidden Semi-Markov model, with Application to Multivariate Financial Data by Zekun Xu, Ye Liu

“…To address this issue, an augmented EM algorithm is developed for parameter estimation by using regularized estimators for the state-dependent covariance matrices and autoregression matrices in the M-step. …”
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LAPM: The Location Aware Prediction Model in Human Sensing Systems by Ruiyun Yu, Pengfei Wang, Shiyang Liao

“…The simulations and real-world case studies are also developed to verify the reliability of the model and the effectiveness of the Location Aware EM algorithm.…”
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Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment by Zhan Gao, Qi-guo Hu, Xiang-yang Xu

“…Moreover, according to the degradation monitoring data of the same batch of equipment, we apply the expectation maximization (EM) algorithm to estimate the prior distribution of the model. …”
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Application of Finite Mixture of Logistic Regression for Heterogeneous Merging Behavior Analysis by Gen Li

“…This model can automatically provide useful hidden information about the characteristics of the driver population. EM algorithm and Newton-Raphson algorithm were used to estimate the parameters. …”
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Level Set Method for Positron Emission Tomography by Tony F. Chan, Hongwei Li, Marius Lysaker, Xue-Cheng Tai

“…In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. …”
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Likelihood Inference of Nonlinear Models Based on a Class of Flexible Skewed Distributions by Xuedong Chen, Qianying Zeng, Qiankun Song

“…However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. …”
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Expectation-Maximization Algorithm of Gaussian Mixture Model for Vehicle-Commodity Matching in Logistics Supply Chain by Qi Sun, Liwen Jiang, Haitao Xu

“…The vehicle classification model is built as a Gaussian mixture model (GMM), and the expectation-maximization (EM) algorithm is designed to solve the parameter estimation of GMM. …”
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Bayesian Network-Based Knowledge Graph Inference for Highway Transportation Safety Risks by Luo Wenhui, Cai Fengtian, Wu Chuna, Meng Xingkai

“…Later, the network parameters are trained via the expectation-maximization (EM) algorithm. Finally, knowledge about highway transportation safety risks is inferred using the junction tree algorithm. …”
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Gaussian decomposition method for full waveform data of LiDAR base on neural network by Jie Liu, Xinjie Zhang, Jing Lv, Xinyu Li, Libin Du

“…First, The FWDN network preprocesses the full waveform data to enhance signal quality by reducing noise, and then the improved EM algorithm extracts Gaussian parameters (amplitude, expectation, and full width at half maximum) to obtain ranging information. …”
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