Bayesian Hierarchical Mixture Models Professors in Global

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Yanan Fan
University of New South Wales Australia
bayesian computation
bayesian inference
bayesian nonparametrics
approximate bayesian computation
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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
hierarchical modeling
bayesian methods
approximate bayesian methods
machine learning
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Edson Utazi
University of Southampton United Kingdom
bayesian computation
bayesian inference
machine learning
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Alexander Hartemink
Duke University Singapore
bayesian networks
bayesian statistics
machine learning
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Paul Arora
University of Toronto Canada
bayesian networks
bayesian statistics
machine learning
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Nanwei Wang
University of New Brunswick Canada
bayesian model selection
linear mixed models
machine learning
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Xenia Miscouridou
Imperial College London United Kingdom
bayesian statistical machine learning
bayesian nonparametrics
deep generative models
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Sara Wade
University of Edinburgh United Kingdom
mixture models
bayesian nonparametrics
machine learning
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Sara Wade
University of Edinburgh United Kingdom
mixture models
bayesian nonparametrics
machine learning
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Umberto Picchini
Chalmers University of Technology Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
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Umberto Picchini
University of Gothenburg Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
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Robin Ryder
Imperial College London United Kingdom
bayesian computational statistics
approximate bayesian computation
stochastic modelling
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Takoua Jendoubi
University College London United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic models
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
machine learning
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Theo Damoulas
University of Warwick United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic machine learning
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Jaehee Kim
Cornell University Netherlands
bayesian inference
mathematical models
machine learning
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Jaroslav Fowkes
University of Oxford United Kingdom
bayesian optimization
statistical pattern mining
machine learning
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Charbel Farhat
Stanford University United States of America
bayesian optimization
mathematical models
probabilistic machine learning
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Daniel Laydon
Imperial College London United Kingdom
bayesian statistics
stochastic modelling
mathematical modelling
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Zoubin Ghahramani
University of Cambridge United Kingdom
bayesian statistics
machine learning
probabilistic modelling
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Geoff Pleiss
University of British Columbia Canada
bayesian optimization
machine learning
probabilistic machine learning
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