Bayesian Graph Structure Learning Professors in Global

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Debdeep Pati
University of Wisconsin–Madison United States of America
bayesian graphical models
bayesian methods
machine learning
efficient bayesian computation
approximate bayesian methods
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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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Yang Xiang
University of Guelph Canada
bayesian networks
graph-based modelling
machine learning
graphical models
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Sharmistha Guha
Texas A&M University United States of America
bayesian object-oriented regression
bayesian high dimensional regression
bayesian causal inference
bayesian nonparametric mixture models
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Danai Koutra
University of Michigan United States of America
graph machine learning
graph neural networks
graph mining
network representation learning
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Edson Utazi
University of Southampton United Kingdom
bayesian computation
bayesian inference
machine learning
spatiotemporal modeling
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Jose Pena
Linköping University Sweden
bayesian networks
probabilistic graphical models
machine learning
chain graphs
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Alexander Hartemink
Duke University Singapore
bayesian networks
bayesian statistics
machine learning
graphical models
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Thomas Nielsen
Aalborg University Denmark
bayesian networks
probabilistic graphical models
machine learning
decision graphs
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Paul Arora
University of Toronto Canada
bayesian networks
bayesian statistics
machine learning
graphical models
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Pan Li
Georgia Institute of Technology United States of America
graph machine learning
graph neural networks
machine learning
geometric deep learning
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Mayank Chadha
University of California, San Diego United States of America
bayesian optimization
bayesian decision theory
machine learning
geometrically exact modeling
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Nanwei Wang
University of New Brunswick Canada
bayesian model selection
machine learning
statistical learning
graphical models
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Yee Teh
University of Oxford United Kingdom
bayesian statistics
bayesian nonparametrics
machine learning
probabilistic learning
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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 inference
bayesian theory
bayesian nonparametrics
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Vida Dujmovic
University of Ottawa Canada
geometric graph theory
structural graph theory
graph theory
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Davide Arioldi
Università della Svizzera italiana Switzerland
bayesian neural networks
probabilistic graphical models
causal structure learning
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Xenia Miscouridou
Imperial College London United Kingdom
bayesian statistical machine learning
bayesian nonparametrics
statistical network modeling
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Johan Kwisthout
Radboud University Netherlands
bayesian networks
probabilistic graphical models
approximate bayesian inference
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Geoff Webb
Monash University Australia
bayesian machine learning
machine learning
graphical models
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