Robust Variable Selection via Bayesian LASSO-Composite Quantile Regression with Empirical Likelihood: A Hybrid Sampling Approach by Ruisi Nan, Jingwei Wang, Hanfang Li, Youxi Luo

“…By constructing a hybrid sampling mechanism that incorporates the Expectation–Maximization (EM) algorithm and Metropolis–Hastings (M-H) algorithm within the Gibbs sampling scheme, this approach effectively tackles variable selection in high-dimensional settings with outlier contamination. …”
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Adaptive Template Attacks on the Kyber Binomial Sampler by Eric Chun-Yu Peng, Markus G. Kuhn

“…We interpret the model from the profiling devices as a Gaussian mixture and use the Expectation–Maximization (EM) algorithm to adapt its means and covariances to better match the unlabelled leakage distribution observed from the attacked setting. …”
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Robust Adaptive Multiple Backtracking VBKF for In-Motion Alignment of Low-Cost SINS/GNSS by Weiwei Lyu, Yingli Wang, Shuanggen Jin, Haocai Huang, Xiaojuan Tian, Jinling Wang

“…Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. …”
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Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model by Haichang Yin, KinTak U, Jing Wang, Wuyue Ma

“…Despite their success, there are still some limitations: (1) A simple Gaussian distribution assumption in the latent space constrains performance due to the diverse distribution of forged images. (2) Gaussian Mixture Models (GMMs) introduce non-convex log-sum-exp functions in the Kullback–Leibler (KL) divergence term, leading to gradient instability and convergence issues during training. (3) Estimating GMM mixing coefficients typically involves either the expectation-maximization (EM) algorithm before VAE training or a multilayer perceptron (MLP), both of which increase computational complexity. …”
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Employing a Mixture of Rayleigh and Inverse Gaussian Distributions as SAR Clutter Texture Model in MAP Estimation for Efficient Speckle Suppression by Dheeren Ku Mahapatra, Saurav Gupta, Biswajit Jena, Ravi Prakash Dwivedi

“…We estimate the RIG mixture model parameters by maximum likelihood (ML) with the expectation maximization (EM) algorithm. We also obtain the Crámer-Rao Bounds (CRBs) for these estimators. …”
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A THIRD GENERATION TOMOGRAPHY SYSTEM WITH FIFTEEN DETECTORS SIMULATED BY MONTE CARLO METHOD by Alexandre Gimenez Alvarez, Alexandre França Velo, Vagner Fernandez, Samir L. Somessari, Francisco F. Sprenger, Margarida M. Hamada, Carlos Henrique de Mesquita

“…The data obtained by the simulation was used to reconstruct the simulated phantom using the statistical iterative Maximum Likelihood Estimation Method Technique (ML-EM) algorithm. Each detector provides a gamma spectrum of the sources, and a pulse height analyzer (PHA) of 10% on the 316.5 KeV and 662 KeV photopeaks was performed. …”
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Immunogenetic characteristics of potential donors of hemapoietic stem cells recruited in the North Caucasus by M. A. Loginova, D. N. Smirnova, S. S. Kutyavina, O. A. Makhova, K. P. Kashin, I. V. Paramonov

“…HLA allele and haplotype frequencies were estimated via maximum-likelihood analysis from genotypic data through an expectationmaximization (EM) algorithm for unknown gametic phase using Arlequin v. 3.5.2.2. …”
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Experimental verification of subthreshold laser therapy using conventional pattern scan laser. by Tomoyasu Shiraya, Satoshi Kato, Fumiyuki Araki, Takashi Ueta, Hitoshi Abe, Nobuharu Asai

“…<h4>Conclusion</h4>These findings suggest that it may be possible to perform subthreshold laser therapy using approximations from the EM algorithm.…”
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High resolution human leukocyte antigen class I allele frequencies and HIV-1 infection associations in Chinese Han and Uyghur cohorts. by Yanhou Liu, Zhongfang Zhao, Tianyi Li, Qi Liao, Nicholas Kushner, Neal Y Touzjian, Yiming Shao, Yongtao Sun, Amie J Strong, Yichen Lu

“…We observed statistically significant increases in expectation-maximization (EM) algorithm predicted haplotype frequencies of HLA-A*0201-B*5101 in the Uyghur HIV-1 negative group, and of Cw*0304-B*4001 in the Han HIV-1 negative group. …”
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Detection of square wave impulse interference in eddy current rail defectograms by Leonid Y. Bystrov, Artemy N. Gladkov, Egor V. Kuzmin

“…The application of the criterion based on SW-characteristic was demonstrated on the examples of eddy current defectograms; a comparison was made with criteria based on the EM-algorithm and multiscale disperse entropy. The criterion proposed in the article showed the best results. …”
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Estimating genomic relationships of metafounders across and within breeds using maximum likelihood, pseudo-expectation–maximization maximum likelihood and increase of relationships... by Andres Legarra, Matias Bermann, Quanshun Mei, Ole F. Christensen

“…Approximating the first derivative by its first term results in a pseudo-EM algorithm that iteratively updates the estimate of $${\varvec{\Gamma}}$$ Γ by the corresponding block of the H-matrix. …”
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A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example by I. D. Busov, M. A. Genaev, E. G. Komyshev, V. S. Koval, T. E. Zykova, A. Y. Glagoleva, D. A. Afonnikov

“…The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann–Whitney U-criterion and Pearson’s criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. …”
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KONVERGENSI ESTIMATOR DALAM MODEL MIXTURE BERBASIS MISSING DATA by N Dwidayati, SH Kartiko, Subanar -

“…One of method which can be applied is Algoritma EM, This method based on 2 ( two) step, that is: ( 1) Expectation Step and ( 2) Maximization Step. EM Algorithm is approach of iteration to study model from data with value loses through 4 ( four) step, yaitu(1) select;chooses initial gathering from parameter for a model, ( 2) determines expectation value for data to lose, ( 3) induce newfangled parameter from value aliance ekspekstasi and original data, and ( 4) if parameter is not converged, repeats step of 2 to apply newfangled.  …”
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Aryana-bs: context-aware alignment of bisulfite-sequencing reads by Hassan Nikaein, Ali Sharifi-Zarchi, Afsoon Afzal, Saeedeh Ezzati, Farzane Rasti, Hamidreza Chitsaz, Govindarajan Kunde-Ramamoorthy

“…While the Expectation-Maximization (EM) algorithm provides only modest initial improvements, it establishes a valuable framework for future refinement and potential enhancements in sensitive applications.…”
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A quasi affine transformation evolution algorithm with evolution matrix selection operation for parameter estimation of proton exchange membrane fuel cells by Mohammad Aljaidi, Pradeep Jangir, Sunilkumar P. Agrawal, Sundaram B. Pandya, Anil Parmar, Samar Hussni Anbarkhan, Laith Abualigah

“…Results show that the QUATRE-EMS algorithm reduces SSE significantly, with an average SSE of 0.078492, which is 15% less than the best performing existing algorithms. …”
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Comparison of principal component analysis algorithms for imputation in agrometeorological data in high dimension and reduced sample size. by Valter Cesar de Souza, Sergio Augusto Rodrigues, Luís Roberto Almeida Gabriel Filho

“…The aim of this study was to evaluate the performance of alternative multivariate procedures for principal component analysis (PCA), using the Nonlinear Iterative Partial Least Squares (NIPALS) and Expectation-Maximization (EM) algorithms, for imputing missing data in time series of meteorological variables. …”
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Predicting Bank Loan Risks Using Machine Learning Algorithms by Maan Alsaleem, Safwan Hasoon

“…In this paper, the performance of machine learning algorithms has been compared for the purpose of classifying bank loan risks using the standard criteria and then choosing (<em>Multilayer Perceptron</em>) as it has given best accuracy compared to <em>RandomForest</em>, <em>BayesNet, NaiveBayes</em> and <em>DTJ48</em> algorithms.…”
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Energy Management of a 1 MW Photovoltaic Power-to-Electricity and Power-to-Gas for Green Hydrogen Storage Station by Dalila Hidouri, Ines Ben Omrane, Kassmi Khalil, Adnen Cherif

“…The proposed system architecture is governed by an innovative energy optimization and management (EMS) algorithm, allowing forecasting, control, and supervision of various PV–hydrogen–Grid transfer scenarios. …”
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An Optimal Energy Management Strategy With Time-Varying Equivalent Factor Based on Transformer for Multi-Mode Hybrid Electric Mining Trucks by Xinxin Zhao, Jiaqi Li, Menglei Liu, Jinggang Zhang, Nasser Lashgarian Azad

“…Furthermore, our strategy exhibits a 2&#x0025; fuel economy advantage over an LSTM-based EMS algorithm with time-varying EF.…”
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Integrated DDPG-PSO energy management systems for enhanced battery cycling and efficient grid utilization by Oladimeji Ibrahim, Mohd Junaidi Abdul Aziz, Razman Ayop, Wen Yao Low, Nor Zaihar Yahaya, Ahmed Tijjani Dahiru, Temitope Ibrahim Amosa, Shehu Lukman Ayinla

“…Cost evaluations under critical peak tariffs indicate that both EMS algorithms achieved a 34 % cost saving compared to a grid-only system. …”
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