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Bayesian Hierarchical Modelling
Bayesian Hierarchical Modelling Professors in Global
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Professor
Debdeep Pati
University of Wisconsin–Madison
United States of America
bayesian graphical models
hierarchical modeling
bayesian methods
machine learning
approximate bayesian methods
efficient bayesian computation
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Professor
Michael Pyrcz
University of Texas at Austin
United States of America
machine learning
spatiotemporal modeling
multivariate modeling
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Professor
Gabriela Czanner
University of Southampton
United Kingdom
spatial statistical models
statistical machine learning models
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Professor
David Taborda
Imperial College London
United Kingdom
geomechanical modelling
numerical modelling
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Professor
Erik Larsson
Linköping University
Sweden
probabilistic models
predictive spatio-temporal models
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Professor
Xenia Miscouridou
Imperial College London
United Kingdom
bayesian statistical machine learning
bayesian nonparametrics
stochastic process modeling
statistical network modeling
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Professor
Mahmoud Alzoubi
Queen's University
Canada
geomechanical modeling
numerical modeling
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Professor
Weronika Gorczyk
University of Western Australia
Australia
geological modelling
numerical modelling
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Professor
Nicole Augustin
University of Edinburgh
United Kingdom
spatial models
spatio-temporal models
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Professor
Nicole Augustin
University of Edinburgh
United Kingdom
spatial models
spatio-temporal models
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Professor
Denis Zorin
New York University
United States of America
geometric modeling
variational modeling
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Professor
Quan Zhou
Texas A&M University
United States of America
bayesian methodology
iterative algorithms
stochastic optimization
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Professor
Huan Lei
Michigan State University
United States of America
stochastic simulations
multi-scale modeling
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Professor
Spyridon Pougkakiotis
King's College London
United Kingdom
large-scale deterministic optimisation
stochastic optimisation
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Professor
Arson Lab
Cornell University
Netherlands
numerical modeling
multi-scale modeling
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Professor
Rajesh Ranganath
New York University
United States of America
machine learning
probabilistic inference
deep generative modeling
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Professor
Aaron Towne
University of Michigan
United States of America
data-driven modeling
reduced-complexity modeling
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Professor
Andreas Daffertshofer
Vrije Universiteit Amsterdam
Netherlands
stochastic neural models
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Professor
Brani Vidakovic
Texas A&M University
United States of America
bayesian statistical methodology
multiscale statistical modeling
statistical modeling
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Professor
Helen Byrne
University of Oxford
United Kingdom
mathematical computational models
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Professor
Franco Momoli
University of Ottawa
Canada
quantitative bias modeling
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