Structural Online Damage Identification and Dynamic Reliability Prediction Method Based on Unscented Kalman Filter by Yan Zhang, Yongbo Zhang, Jinhui Yu, Fei Zhao, Shihao Zhu

“…Following that, the EM algorithm is employed for estimating the performance model parameters. …”
Get full text

MODELING THE MANY EARTHQUAKES IN SUMATRA USING POISSON HIDDEN MARKOV MODELS AND EXPECTATION MAXIMIZATION ALGORITHM by Muhammad Arib Alwansyah, Ramya Rachmawati

“…The Poisson Hidden Markov Models (PHMMs) method is used to overcome overdispersion, then applying the Expectation-Maximization Algorithm (EM algorithm) to each model to obtain the estimated parameters. …”
Get full text

Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data. by Qihai Liu, Kevin H Lee, Hyun Bin Kang

“…We further design an estimation method for MFGM using an iterative Expectation-Maximization (EM) algorithm and functional graphical lasso (fglasso). …”
Get full text

Improved Online Kalman Smoothing Method for Ship Maneuvering Motion Data Using Expectation-Maximization Algorithm by Wancheng Yue, Junsheng Ren

“…We propose an online smoothing method enhanced by the Expectation-Maximization (EM) algorithm framework that effectively extracts high-fidelity dynamic features from raw maneuvering data, thereby enhancing the fidelity of subsequent ship identification systems. …”
Get full text

Finite Mixture Model-Based Analysis of Yarn Quality Parameters by Esra Karakaş, Melik Koyuncu, Mülayim Öngün Ükelge

“…Model parameters are estimated using the expectation–maximization (EM) algorithm, and model selection is guided by the Akaike and Bayesian information criteria (AIC and BIC). …”
Get full text

A hierarchical statistical model for estimating population properties of quantitative genes by Samuel S Wu, Chang-Xing Ma, Rongling Wu, George Casella

“…The model is implemented with an EM algorithm to provide maximum likelihood estimates for genetic parameters of the major locus. …”
Get full text

Inferences for Skew-Wiener Degradation Model: Approximate Bayesian Computation via Gibbs-Step and Model Selection by Isyaku Muhammad, Mustapha Muhammad, Ren Aihua, Aiqiang Zhang

“…This study introduces a robust parameter estimation method for the Skew-Wiener model using Approximate Bayesian Computation with Gibbs sampling (ABC-Gibbs), providing an alternative to the Expectation-Maximization (EM) algorithm. ABC-Gibbs offers a computationally efficient, likelihood-free inference approach, particularly well-suited for high-dimensional parameter spaces where direct likelihood calculations are computationally expensive. …”
Get full text

Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model by Fusheng Zha, Yizhou Liu, Xin Wang, Fei Chen, Jingxuan Li, Wei Guo

“…The influence of number of Gaussian components on the fitting accuracy is analyzed in detail, and a self-adaptive model training method based on Greedy expectation-maximization (EM) algorithm is proposed. At the same time, this method has the capability of online updating and can eliminate model fitting errors due to environmental changes. …”
Get full text

Bistatic ISAR Sparse Imaging Method for High-Speed Moving Target Based on Dechirping Processing by Chuangzhan Zeng, Weigang Zhu, Xin Jia

“…Finally, with the sparsity of the scattering points, the required parameters are solved using the expectation maximization (EM) algorithm based on the maximum a posteriori probability criterion. …”
Get full text

A New Method of Remaining Useful Lifetime Estimation for a Degradation Process with Random Jumps by Yue Zhuo, Lei Feng, Jianxun Zhang, Xiaosheng Si, Zhengxin Zhang

“…In addition, a general model identification approach is presented based on maximization likelihood estimation (MLE), and an iterative model identification approach is provided based on the expectation maximization (EM) algorithm. Finally, the practical value and effectiveness of the proposed method are validated using real-world degradation data from temperature sensors on a blast furnace wall. …”
Get full text

A novel damage detection method based on sequential iteration and Gaussian mixture model for structural health monitoring under environmental effects by Jie-zhong Huang, Jian Yang, Dong-sheng Li, Wei-chen Kong, Ya-fei Wang

“…In the second step, the expectation-maximization (EM) algorithm is used to establish the GMM, clustering the training data into local subsets. …”
Get full text

Nanodesigner: resolving the complex-CDR interdependency with iterative refinement by Melissa Maria Rios Zertuche, Şenay Kafkas, Dominik Renn, Magnus Rueping, Robert Hoehndorf

“…NanoDesigner integrates key stages—structure prediction, docking, CDR generation, and side-chain packing—into an iterative framework based on an expectation maximization (EM) algorithm. The algorithm effectively tackles an interdependency challenge where accurate docking presupposes a priori knowledge of the CDR conformation, while effective CDR generation relies on accurate docking outputs to guide its design. …”
Get full text

Towards a harmonized operational earthquake forecasting model for Europe by M. Han, L. Mizrahi, S. Wiemer

“…We propose a method modification that integrates information from the 2020 European Seismic Hazard Model (ESHM20) about the spatial variation in background seismicity during ETAS parameter inversion based on the expectation–maximization (EM) algorithm. Other modifications to the basic ETAS model are explored, namely fixing the productivity term to a higher value to balance the more productive triggering by high-magnitude events with their much rarer occurrence and replacing the <span class="inline-formula"><i>b</i></span>-value estimate with one relying on the <span class="inline-formula"><i>b</i></span>-positive method to observe the possible effect of short-term incompleteness on model parameters. …”
Get full text

Accurate identification of locally aneuploid cells by incorporating cytogenetic information in single cell data analysis by Ziyi Li, Ruoxing Li, Irene Ganan-Gomez, Hussein A. Abbas, Guillermo Garcia-Manero, Wei Sun

“…PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. …”
Get full text

Inferring the Timing of Antiretroviral Therapy by Zero-Inflated Random Change Point Models Using Longitudinal Data Subject to Left-Censoring by Hongbin Zhang, McKaylee Robertson, Sarah L. Braunstein, David B. Hanna, Uriel R. Felsen, Levi Waldron, Denis Nash

“…We extend the Stochastic EM (StEM) algorithm by combining a Gibbs sampler with a Metropolis–Hastings sampling. …”
Get full text

Cluster analysis of the results of intraoperative optical spectroscopic diagnostics In brain glioma neurosurgery by I. A. Osmakov, T. A. Savelieva, V. B. Loschenov, S. A. Goryajnov, A. A. Potapov

“…It was shown that, among the considered clustering methods, EM‑algorithm and k‑means methods are optimal for the considered data set and can be used to build a decision support system (DSS) for spectroscopic intraoperative navigation in neurosurgery. …”
Get full text

Estimation and diagnostic for single-index partially functional linear regression model with $ p $-order autoregressive skew-normal errors by Lijie Zhou, Liucang Wu, Bin Yang

“…Additionally, we conducted residual analysis based on conditional quantiles, considering the skew-normal distribution and autocorrelation of the residuals, and performed local influence analysis using the Q-function in the EM algorithm. The efficiency of the EM-CALS algorithm was validated through simulation studies. …”
Get full text

Mechanical Vibration Signal Denoising Using Quantum-Inspired Standard Deviation Based on Subband Based Gaussian Mixture Model by Aidong Xu, Wenqi Huang, Peng Li, Huajun Chen, Jiaxiao Meng, Xiaobin Guo

“…Then, within Bayesian framework, the maximum a posteriori (MAP) estimator is employed to derive a thresholding function with conventional standard deviation (CSD) which is calculated by the expectation-maximization (EM) algorithm. However, the CSD has a disadvantage of ignoring the interscale dependency between wavelet coefficients. …”
Get full text

Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data by Hua Wang, Changlong Gu, Washington Yotto Ochieng

“…The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm. The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory. …”
Get full text

Extended target tracking with mobility based on GPR-AUKF by Renli Zhang, Yan Zhang, Jintao Chen, Ziwen Sun, Jing Li, Zhuangbin Tan, Zhongxing Jiao

“…Specifically, the GPR-AUKF algorithm is built based on expectation maximization (EM) algorithm to track the target state and covariance, and which updates the measurement noise covariance in real-time. …”
Get full text