Results 11 to 20 of about 736,978 (266)
A family of mixture models for biclustering [PDF]
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractBiclustering is used for simultaneous clustering of the observations and variables when there is no group structure known a priori. It is being increasingly used in bioinformatics, text analytics, and so on. Previously, biclustering has been introduced in a model‐based clustering framework by utilizing a structure similar to a mixture of factor
Wangshu Tu, Sanjeena Subedi
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Frontiers in Nutrition, 2021 This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is
Alan Russell +2 more
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A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning
Judgment and Decision Making, 2017 People often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to
Arthur Kary +3 more
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flexCWM: A Flexible Framework for Cluster-Weighted Models
Journal of Statistical Software, 2018 Cluster-weighted models (CWMs) are mixtures of regression models with random covariates. However, besides having recently become rather popular in statistics and data mining, there is still a lack of support for CWMs within the most popular statistical ...
Angelo Mazza +2 more
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Deep Gaussian mixture models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables ...
Cinzia Viroli, Geoffrey J. McLachlan
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IEEE Access, 2018 The destructive effects of cyber-attacks demand more proactive security approaches. One such promising approach is the idea of collaborative intrusion detection systems (CIDSs).
Carlos Garcia Cordero +6 more
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How to fit models of recognition memory data using maximum likelihood.
International Journal of Psychological Research, 2010 The aim of this paper is to provide an introductory tutorial to how to fit different models of recognition memory using maximum likelihood estimation. It is in four main parts.
John C. Dunn
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CoRR, 2016 Casting neural networks in generative frameworks is a highly sought-after endeavor these days. Contemporary methods, such as Generative Adversarial Networks, capture some of the generative capabilities, but not all. In particular, they lack the ability of tractable marginalization, and thus are not suitable for many tasks.
Or Sharir +3 more
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Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer
Journal of Innovation Information Technology and Application, 2023 The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela +4 more
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, 2019 Mixtures of experts models provide a framework in which covariates may be included in mixture models. This is achieved by modelling the parameters of the mixture model as functions of the concomitant covariates. Given their mixture model foundation, mixtures of experts models possess a diverse range of analytic uses, from clustering observations to ...
Gormley, Isobel Claire +1 more
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