Latent Growth Mixture Models Professors in Global

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Ken Beath
Macquarie University Australia
latent variable models
mixture models
infant growth models
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Sharon Christ
Purdue University United States of America
mixed-effect models
latent variables
longitudinal modeling
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Booil Jo
Stanford University United States of America
mixture growth modeling
latent variable modeling
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Bengt Muthén
University of California Los Angeles United States of America
growth mixture modeling
latent variable analysis
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Yuqi Gu
Columbia University United States of America
latent variable models
deep generative models
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Bryon Aragam
University of Chicago Booth School of Business United States of America
latent variable models
deep generative models
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Camila De Souza
Western University Canada
hierarchical mixture models
mixed effect models
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Alexander Donev
University of Manchester United Kingdom
linear mixed effect models
non-linear mixed effect models
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Maggie Niu
Pennsylvania State University United States of America
latent variable models
network models
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James Martin
University of Oxford United Kingdom
random growth models
fragmentation models
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Georges Monette
York University Canada
mixed models
longitudinal models
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Ziyang Lyu
University of New South Wales Australia
mixed models
finite gaussian mixture models
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Zachary Kunicki
Brown University United States of America
latent variable modeling
growth curve modeling
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Alexander De Leon
University of Calgary Canada
mixed effects models
joint models
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Florentina Bunea
Cornell Bowers University United States of America
mixture modeling
latent space estimation
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Bei Jiang
University of Alberta Canada
mixture modeling
joint modeling
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Alexander De Leon
University of Calgary Canada
mixed effects models
joint models
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Rosane Nisenbaum
University of Toronto Canada
latent transition analysis
latent class analysis
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David Choi
Carnegie Mellon University United States of America
latent variables
network models
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Samuel Paskewitz
Yale University United States of America
bayesian latent variable models
computational models
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Nabarun Deb
University of Chicago United States of America
generative modeling
network models
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