Bayesian Systems Professors in Global

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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian methods
bayesian graphical models
approximate bayesian methods
efficient bayesian computation
machine learning
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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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Alexander Hartemink
Duke University Singapore
bayesian networks
bayesian statistics
machine learning
systems biology
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Sharmistha Guha
Texas A&M University United States of America
bayesian high dimensional regression
bayesian causal inference
bayesian object-oriented regression
bayesian nonparametric mixture models
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Peter Mueller
University of Texas at Austin United States of America
bayesian bioinformatics
computational methods in bayesian inference
nonparametric bayesian inference
bayesian adaptive clinical trial design
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Hina Shaheen
University of Manitoba Canada
bayesian inference
machine learning
statistical machine learning
computational modelling
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Trevor Campbell
University of British Columbia Canada
bayesian theory
bayesian inference
bayesian nonparametrics
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Edson Utazi
University of Southampton United Kingdom
bayesian computation
bayesian inference
machine learning
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Paul Arora
University of Toronto Canada
bayesian networks
bayesian statistics
machine learning
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Mayank Chadha
University of California, San Diego United States of America
bayesian optimization
bayesian decision theory
machine learning
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
machine learning
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François Caron
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
statistical machine learning
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Takoua Jendoubi
University College London United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic models
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Theo Damoulas
University of Warwick United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic machine learning
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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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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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David Matteson
Cornell University Netherlands
bayesian analysis
machine learning
stochastic processes
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Jaehee Kim
Cornell University Netherlands
bayesian inference
machine learning
mathematical models
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James Beck
California Institute of Technology United States of America
bayesian updating
computational bayesian statistics
stochastic system modeling
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