Bayesian Sampling 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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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
randomized trials
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
machine learning
probabilistic learning
large scale machine learning
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Yanan Fan
University of New South Wales Australia
bayesian computation
bayesian inference
approximate bayesian computation
bayesian nonparametrics
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Umberto Picchini
University of Gothenburg Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
statistical inference
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Umberto Picchini
Chalmers University of Technology Sweden
bayesian computational methods
approximate bayesian computation
stochastic modelling
statistical inference
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Nick Whiteley
University of Bristol United Kingdom
bayesian modelling
sampling methods
probabilistic modelling
statistical inference
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John Lau
University of Western Australia Australia
bayesian statistics
bayesian nonparametric statistics
statistical filtering
statistical clustering
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Bradley Efron
Stanford University United States of America
bayesian statistics
bootstrap sampling
machine learning
statistical inference
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Hina Shaheen
University of Manitoba Canada
bayesian inference
machine learning
statistical machine learning
computational modelling
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Gareth Roberts
University of Warwick United Kingdom
bayesian statistics
stochastic simulation
statistical inference
stochastic processes
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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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Dabao Zhang
Purdue University United States of America
bayesian
machine learning
statistical inference
data mining
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Michael Zhu
Purdue University United States of America
bayesian computational methods
machine learning
statistical inference
data mining
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Madhuchhanda Bhattacharjee
University of Manchester United Kingdom
bayesian integrated modelling
statistical inference
regression modelling
stochastic processes
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Fotios Kokkotos
Boston University United States of America
sampling theory
machine learning
stochastic processes
data mining
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Eric Xing
Carnegie Mellon University United States of America
probabilistic modeling
machine learning
statistical learning
data mining
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Laurence Frank
Utrecht University Netherlands
machine learning
predictive modelling
randomized response
statistical learning
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Trevor Campbell
University of British Columbia Canada
bayesian inference
bayesian theory
bayesian nonparametrics
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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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