Search Results - "Variational expectation-maximisation algorithm"

A variational Expectation–Maximization algorithm for temporal data clustering by El Assaad, Hani, Samé, Allou, Govaert, Gérard, Aknin, Patrice

“…The problem of temporal data clustering is addressed using a dynamic Gaussian mixture model. In addition to the missing clusters used in the classical Gaussian…”
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Community detection for multilayer weighted networks by Chen, Yan, Mo, Dongxu

“… A variational expectation-maximisation algorithm is derived to estimate the parameters of interest…”
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Variational Bayesian learning for background subtraction based on local fusion feature by Yan, Junhua, Wang, Shunfei, Xie, Tianxia, Yang, Yong, Wang, Jiayi

“…To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a…”
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A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems by Balenzuela, Mark P, Wills, Adrian G, Renton, Christopher, Ninness, Brett

“…Jump Markov linear systems (JMLS) are a useful class which can be used to model processes which exhibit random changes in behavior during operation. This paper…”
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Compressive high-range-resolution radar imaging using dynamic dictionaries by Hu, Lei, Shi, Zhiguang, Zhou, Jianxiong, Fu, Qiang

“…Compressive sensing theory suggests that accurate reconstruction of a signal can be achieved using its highly undersampled measurements, provided that the…”
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Estimation in a binomial stochastic blockmodel for a weighted graph by a variational expectation maximization algorithm by Haj, Abir El, Slaoui, Yousri, Louis, Pierre-Yves, Khraibani, Zaher

“…Stochastic blockmodels have been widely proposed as a probabilistic random graph model for the analysis of networks data as well as for detecting community…”
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Hybrid Genetic and Variational Expectation-Maximization Algorithm for Gaussian-Mixture-Model-Based Brain MR Image Segmentation by Tian, GuangJian, Xia, Yong, Zhang, Yanning, Feng, Dagan

“…The expectation-maximization (EM) algorithm has been widely applied to the estimation of Gaussian mixture model (GMM) in brain MR image segmentation. However,…”
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Glaucoma progression detection using variational expectation maximization algorithm by Belghith, Akram, Balasubramanian, Madhusudhanan, Bowd, Christopher, Weinreb, Robert N., Zangwill, Linda M.

“…Glaucoma, the second leading cause of blindness worldwide, is an optic neuropthy characterized by distinctive changes in the optic nerve head (ONH) and visual…”
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A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter by Yan, Yujia, Hu, Cheng, Cai, Jiong, Li, Weidong, Yu, Teng, Wang, Rui

“…The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the…”
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Maximum likelihood estimation for discrete latent variable models via evolutionary algorithms by Brusa, Luca, Pennoni, Fulvia, Bartolucci, Francesco

“…We propose an evolutionary optimization method for maximum likelihood and approximate maximum likelihood estimation of discrete latent variable models. The…”
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Anomaly detection for fabricated artifact by using unstructured 3D point cloud data by Tao, Chengyu, Du, Juan, Chang, Tzyy-Shuh

“…3D point cloud data has been widely used in surface quality inspection to measure fabricated artifacts, allowing the high density and precision of measurements…”
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Variational inference for estimating dynamic stochastic block models through an evolutionary algorithm by Brusa, Luca, Pennoni, Fulvia

“…Dynamic temporal networks are important structures to capture node dependencies and their evolution over time. The dynamic stochastic block model, commonly…”
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Powerful multiple testing of paired null hypotheses using a latent graph model by Rebafka, Tabea, Roquain, Étienne, Villers, Fanny

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Statistical clustering of temporal networks through a dynamic stochastic block model by Matias, Catherine, Miele, Vincent

“…Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and…”
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Modeling comorbidity of chronic diseases using coupled hidden Markov model with bivariate discrete copula by Oflaz, Zarina, Yozgatligil, Ceylan, Selcuk-Kestel, A Sevtap

“… To estimate the parameters of the model and deal with the numerical intractability of the log-likelihood, we use a variational expectation maximization algorithm…”
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A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations by Shi, Zhuangwei, Zhang, Han, Jin, Chen, Quan, Xiongwen, Yin, Yanbin

“…Background Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential…”
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Blind separation of sparse sources using variational EM by Cemgil, Ali Taylan, Fevotte, Cedric, Godsill, Simon J.

“… The conjugate-exponential characterisation of the t distribution as an infinite mixture of scaled Gaussians enables us to derive an efficient variational expectation maximisation algorithm (V-EM…”
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Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition by Wang, Shiying, Constable, Todd, Zhang, Heping, Zhao, Yize

“…Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological…”
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LSMM: a statistical approach to integrating functional annotations with genome-wide association studies by Ming, Jingsi, Dai, Mingwei, Cai, Mingxuan, Wan, Xiang, Liu, Jin, Yang, Can

“…Abstract Motivation Thousands of risk variants underlying complex phenotypes (quantitative traits and diseases) have been identified in genome-wide association…”
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Variational Approximation-Based Model Selection for Microbial Network Inference by Yooseph, Shibu, Tavakoli, Sahar

“…Microbial associations are characterized by both direct and indirect interactions between the constituent taxa in a microbial community, and play an important…”
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