Bayesian Integrated Modelling Professors in Global

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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
bayesian methods
efficient bayesian computation
machine learning
approximate bayesian methods
hierarchical modeling
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Yanan Fan
University of New South Wales Australia
bayesian computation
bayesian inference
bayesian nonparametrics
approximate bayesian computation
spatial modelling
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Edson Utazi
University of Southampton United Kingdom
bayesian computation
bayesian inference
spatial modeling
machine learning
spatiotemporal modeling
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Sharmistha Guha
Texas A&M University United States of America
bayesian high dimensional regression
bayesian object-oriented regression
bayesian causal inference
bayesian nonparametric mixture models
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Mayank Chadha
University of California, San Diego United States of America
bayesian optimization
bayesian decision theory
geometrically exact modeling
machine learning
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Xenia Miscouridou
Imperial College London United Kingdom
bayesian statistical machine learning
bayesian nonparametrics
infectious disease modeling
stochastic process modeling
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Hina Shaheen
University of Manitoba Canada
bayesian inference
computational modelling
machine learning
statistical machine learning
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Peter Mueller
University of Texas at Austin United States of America
bayesian bioinformatics
nonparametric bayesian inference
computational methods in bayesian inference
bayesian adaptive clinical trial design
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Trevor Campbell
University of British Columbia Canada
bayesian inference
bayesian theory
bayesian nonparametrics
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Michele Guindani
University of California, Irvine United States of America
bayesian modeling
nonparametric bayesian models
machine learning
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Andre Amaral
University of Southampton United Kingdom
bayesian modelling
machine learning
spatio-temporal statistical modelling
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Andrew Mccluskey
University of Bristol United Kingdom
bayesian analysis
mathematical modeling
statistical modeling
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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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Umberto Picchini
Chalmers University of Technology 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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Daniel Laydon
Imperial College London United Kingdom
bayesian statistics
stochastic modelling
mathematical modelling
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Umberto Picchini
University of Gothenburg Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
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Emma Fairbanks
University of Manchester United Kingdom
bayesian inference
mathematical modeling
infectious disease modeling
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
machine learning
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Rohitash Chandra
University of New South Wales Australia
bayesian deep learning
machine learning
geoscientific modeling
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