Search Results - "Variational expectation-maximisation algorithm"

A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems

Authors: Balenzuela, Mark P. Wills, Adrian G. Renton, Christopher et al.

Subject Terms: FOS: Computer and information sciences, 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Applications (stat.AP), 02 engineering and technology, Systems and Control (eess.SY), Statistics - Applications, Electrical Engineering and Systems Science - Systems and Control

Access URL: http://arxiv.org/abs/2004.08564

A variational Expectation Maximisation algorithm for temporal data clustering

Authors: El Assaad, Hani Same, Allou Govaert, Gérard et al.

Contributors: El Assaad, Hani Same, Allou Govaert, Gérard et al.

Source: ISSN: 0167-9473 ; Computational Statistics and Data Analysis ; https://hal.science/hal-01381345 ; Computational Statistics and Data Analysis, 2016, 103, pp.206-228. ⟨10.1016/j.csda.2016.05.007⟩.

Subject Terms: VARIATIONAL APPROXIMATION, DYNAMIC LATENT VARIABLE MODEL, CLUSTERING, TEMPORAL DATA CLUSTERING, ALGORITHME ESPERANCE-MAXIMISATION, FILTRE DE KALMAN, MODELE DE MELANGE, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]

Availability: https://hal.science/hal-01381345
https://doi.org/10.1016/j.csda.2016.05.007

Community detection for multilayer weighted networks.

Authors: Chen, Yan Mo, Dongxu

Source: Information Sciences. May2022, Vol. 595, p119-141. 23p.

Subject Terms: *STOCHASTIC models, *FINANCIAL markets, BICYCLE sharing programs

The Deep Latent Position Block Model for Block Clustering and Latent Representation of Nodes in Networks.

Authors: Boutin, Rémi Latouche, Pierre Bouveyron, Charles

Source: Statistics & Computing; Oct2025, Vol. 35 Issue 5, p1-24, 24p

Variational Bayesian Learning for the Modelling of Indoor Broadband Powerline Communication Impulsive Noise.

Authors: Chelangat, Florence Afullo, Thomas

Source: Progress in Electromagnetics Research B; 2023, Vol. 100, p109-131, 23p

Subject Terms: BROADBAND communication systems, MACHINE learning, BURST noise, NOISE, RANDOM variables

Entrepreneurial self-efficacy among students: a bibliometric mapping and topic modelling approach.

Authors: Anton, Stuwart Mansingh, Paul

Source: Entrepreneurship Education; Jun2025, Vol. 8 Issue 2, p177-203, 27p

Variational Bayesian learning for background subtraction based on local fusion feature.

Authors: Junhua Yan Shunfei Wang Tianxia Xie et al.

Source: IET Computer Vision (Wiley-Blackwell); Dec2016, Vol. 10 Issue 8, p884-893, 10p

Subject Terms: BAYESIAN analysis, MACHINE learning, FEATURE selection, COMPUTATIONAL complexity, GAUSSIAN mixture models

Compressive high‐range‐resolution radar imaging using dynamic dictionaries.

Authors: Hu, Lei Shi, Zhiguang Zhou, Jianxiong et al.

Source: IET Radar, Sonar & Navigation (Wiley-Blackwell). Jun2013, Vol. 7 Issue 5, p497-507. 11p.

Subject Terms: *ENCYCLOPEDIAS & dictionaries, *ANECHOIC chambers, *SIGNAL reconstruction, *IMAGE reconstruction, *RADAR

Demagnetisation fault analysis and diagnosis based on different methods in permanent magnet machines ‐ An overview.

Authors: Faiz, Jawad Bazrafshan, Mohammad Amin Tabarniarami, Zabihollah

Source: IET Electric Power Applications (Wiley-Blackwell); Dec2024, Vol. 18 Issue 12, p1860-1893, 34p

Subject Terms: SYNCHRONOUS electric motors, BRUSHLESS electric motors, PERMANENT magnets, MACHINE performance, POWER density

Data-Driven Insights into Non-Purchasing Behaviours through Latent Dirichlet Allocation: Analysing Study Material Acquisition Among University Students.

Authors: Mosia, Moeketsi

Source: Journal of Culture & Values in Education (JCVE); 2024, Vol. 7 Issue 1, p72-82, 11p

Subject Terms: HIGHER education, SCHOLARLY method, EDUCATIONAL resources, ELECTRONIC textbooks, LEARNING communities, SOCIAL learning, SOCIAL justice, SOCIOECONOMIC factors

Causal relationship of immune cell characteristics in hepatocellular carcinoma: A multi-omics analysis based on Mendelian randomization.

Authors: Tian, XiaoLing Wang, BaoChun Zhang, XinYi et al.

Source: Medicine; 12/5/2025, Vol. 104 Issue 49, pe45942-e45942, 1p

Static and dynamic methods in social network analysis reveal the association patterns of desert-dwelling giraffe.

Authors: Hart, Emma E. Ciuti, Simone Herrmann, Leticia et al.

Source: Behavioral Ecology & Sociobiology; May2022, Vol. 76 Issue 5, p1-16, 16p

Subject Terms: SOCIAL network analysis, GENE flow, SOCIAL networks, INFECTIOUS disease transmission, ANIMAL societies

Blur parameter locus curve and its applications.

Authors: Kumar, Himanshu Gupta, Sumana Subramanian, Venkatesh K.

Source: IET Image Processing (Wiley-Blackwell); Feb2020, Vol. 14 Issue 3, p297-309, 13p

Semantic concept model using Wikipedia semantic features.

Authors: Saif, Abdulgabbar Omar, Nazlia Ab Aziz, Mohd Juzaiddin et al.

Source: Journal of Information Science; Aug2018, Vol. 44 Issue 4, p526-551, 26p

Subject Terms: NATURAL language processing, SEMANTIC computing, INFORMATION theory, ARTIFICIAL neural networks, ARTIFICIAL intelligence

Is impaired cerebral vasoreactivity an early marker of cognitive decline in multiple sclerosis patients?

Authors: Metzger, Aude Le Bars, Emmanuelle Deverdun, Jeremy et al.

Source: European Radiology; Mar2018, Vol. 28 Issue 3, p1204-1214, 11p, 1 Diagram, 3 Charts, 4 Graphs

Subject Terms: MULTIPLE sclerosis, MILD cognitive impairment, MAGNETIC resonance imaging, BRAIN imaging, BRAIN mapping, PATIENTS

Learning a joint discriminative-generative model for action recognition.

Authors: Alexiou, Ioannis Xiang, Tao Gong, Shaogang

Source: 2015 International Conference on Systems, Signals & Image Processing (IWSSIP); 2015, p1-4, 4p

Two problems with variational expectation maximisation for time series models.

Source: Bayesian Time Series Models; 2011, p104-124, 21p

A Variational EM Approach to Predicting Uncertainty in Supervised Learning.

Authors: Harva, M.

Source: 2006 IEEE International Joint Conference on Neural Network Proceedings; 2006, p5342-5346, 5p

Blind separation of sparse sources using variational EM.

Authors: Cemgil, Ali Taylan Fevotte, Cedric Godsill, Simon J.

Source: 2005 13th European Signal Processing Conference; 2005, p1-4, 4p

Sparse probabilistic projections

Authors: Cédric Archambeau Francis R. Bach The Pennsylvania State University CiteSeerX Archives

Contributors: Cédric Archambeau Francis R. Bach The Pennsylvania State University CiteSeerX Archives

Source: http://www.cs.ucl.ac.uk/staff/c.archambeau/publ/nips_ca09.pdf.

File Description: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.9118; http://www.cs.ucl.ac.uk/staff/c.archambeau/publ/nips_ca09.pdf

Availability: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.9118
http://www.cs.ucl.ac.uk/staff/c.archambeau/publ/nips_ca09.pdf