Approximate Bayesian Inference Professors in Global

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Debdeep Pati
University of Wisconsin–Madison United States of America
approximate bayesian methods
bayesian methods
efficient bayesian computation
bayesian graphical models
machine learning
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Yanan Fan
University of New South Wales Australia
approximate bayesian computation
bayesian inference
bayesian computation
bayesian nonparametrics
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Umberto Picchini
Chalmers University of Technology Sweden
approximate bayesian computation
statistical inference
bayesian computational methods
stochastic modelling
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Umberto Picchini
University of Gothenburg Sweden
approximate bayesian computation
statistical inference
bayesian computational methods
stochastic modelling
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Theo Damoulas
University of Warwick United Kingdom
approximate inference methodologies
bayesian statistics
bayesian nonparametrics
probabilistic machine learning
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Abhishek Chakrabortty
Texas A&M University United States of America
bayesian inference
statistical inference
causal inference
machine learning
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Sharmistha Guha
Texas A&M University United States of America
bayesian causal inference
bayesian high dimensional regression
bayesian object-oriented regression
bayesian nonparametric mixture models
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Guanyu Hu
Michigan State University United States of America
bayesian computation
bayesian nonparametric
big data inference
causal inference
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Rajesh Ranganath
New York University United States of America
probabilistic inference
statistical inference
causal inference
machine learning
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
probabilistic learning
machine learning
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Mohammad Anam Khan
University of Toronto Canada
statistical inference
causal inference
biostatistical analysis
machine learning
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Mingyuan Zhou
University of Texas at Austin United States of America
approximate inference
bayesian analysis
probabilistic machine learning
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Trevor Campbell
University of British Columbia Canada
bayesian inference
bayesian theory
bayesian nonparametrics
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Robin Ryder
Imperial College London United Kingdom
approximate bayesian computation
bayesian computational statistics
stochastic modelling
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Xinran Li
University of Chicago United States of America
bayesian inference
randomization-based inference
causal inference
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Edson Utazi
University of Southampton United Kingdom
bayesian inference
bayesian computation
machine learning
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Jaehee Kim
Cornell University Netherlands
bayesian inference
statistical inference
machine learning
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Tom Rainforth
University of Oxford United Kingdom
approximate inference
bayesian experimental design
statistical machine learning
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Ricardo Silva
University College London United Kingdom
bayesian inference
relational inference
machine learning
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Ricardo Silva
University College London United Kingdom
bayesian inference
relational inference
machine learning
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Raaz Dwivedi
Cornell University Netherlands
bayesian inference
causal inference
statistical machine learning
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