Results 11 to 20 of about 3,472,659 (303)
Mixture multilevel vector-autoregressive modeling [PDF]
, 2022 The modeling techniques for intensive longitudinal data are increasingly focused on individual differences.In my talk I will present mixture multilevel vector-autoregressive modeling, which extends multilevel vectorautoregressive modeling by including a ...
Timmerman, Marieke; id_orcid +13 more
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On Choosing a Mixture Model for Clustering [PDF]
Journal of Data Science, 2021 2 Universit e de Caen Abstract: Two methods for clustering data and choosing a mixture model are proposed. First, we derive a new classication algorithm based on the classication likelihood. Then, the likelihood conditional on these clusters is written as the product of likelihoods of each cluster, and AIC- respectively BIC-type approximations are ...
Ngatchou Wandji, Joseph, Bulla, Jan
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Graph Laplacian Mixture Model [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2020 Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph.
Hermina Petric Maretic, Pascal Frossard
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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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Partitioning Pervasive Detrital Geochronologic Age Distributions in the Southern Alaskan Forearc
Frontiers in Earth Science, 2019 The extensive detrital zircon U-Pb geochronologic dataset presented here includes new and compiled data (N = 38; n = 8,006) from modern rivers that together comprehensively characterizes the geographic distribution of pervasive Mesozoic—Cenozoic igneous ...
Emily S. Finzel
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Non parametric mixture modeling
, 2022 Code and data for Archambeau, Couto & Van Maanen (in press) - Non-Parametric Mixture Modeling Of Cognitive Psychological Data : A New Method To disentangle Hidden ...
Leendert van Maanen +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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Estimating propensity scores with missing covariate data using general location mixture models
, 2009 In many observational studies, researchers estimate causal effects using propensity scores, e.g., by matching or sub-classifying on the scores. Estimation of propensity scores is complicated when some values of the covariates aremissing.
Reiter, Jerome P. +3 more
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Video foreground detection based on symmetric alpha-stable mixture models. [PDF]
, 2010 Background subtraction (BS) is an efficient technique for detecting moving objects in video sequences. A simple BS process involves building a model of the background and extracting regions of the foreground (moving objects) with the assumptions that the
Bhaskar, H. +2 more
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Exploring Class Enumeration in Bayesian Growth Mixture Modeling Based on Conditional Medians
Frontiers in Education, 2021 Growth mixture modeling is a popular analytic tool for longitudinal data analysis. It detects latent groups based on the shapes of growth trajectories.
Seohyun Kim, Xin Tong, Zijun Ke
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