Scalable Likelihood Methods Professors in Global

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Haziq Jamil
London School of Economics and Political Science United Kingdom
scalable estimation methods
statistical methods
approximate bayesian methods
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John Anderson
Carnegie Mellon University United States of America
statistical methods
mathematical methods
computational methods
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Dave Touretzky
Carnegie Mellon University United States of America
statistical methods
mathematical methods
computational methods
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Caleb Pomeroy
University of Toronto Canada
statistical methods
computational methods
experimental methods
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Wei Zhang
University of Sydney Australia
likelihood approximation
computational methods
statistical modelling
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Ingemar André
Lund University Sweden
computational methods
experimental methods
high-throughput methods
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Thomas Severini
Northwestern University United States of America
statistical methodology
likelihood-based statistics
maximum likelihood estimation
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Charles Kenley
Purdue University United States of America
modeling and simulation methods
bayesian methods
quantitative methods
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Monique Laurent
Tilburg University Netherlands
combinatorial methods
algebraic methods
geometric methods
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Tom Rainforth
University of Oxford United Kingdom
probabilistic data-efficient approaches
probabilistic programming
statistical machine learning
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Ivan Kryven
Utrecht University Netherlands
numerical methods
randomized algorithms
random approximation algorithms
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Carsten Burstedde
University of Bonn Germany
scalable algorithms
mathematical methods
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Roger Creel
Texas A&M University United States of America
probabilistic methods
scalable open-source computing
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Ani Eloyan
Brown University United States of America
likelihood-based methods
semi-parametric methods
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Sham Kakade
Harvard University United States of America
scalable optimization algorithms
scaling laws
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Purnamrita Sarkar
University of Texas at Austin United States of America
scalable inference algorithms
statistical machine learning
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Christopher Musco
New York University Tandon School of Engineering United States of America
scalable machine learning
randomized algorithms
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Tim Burness
University of Bristol United Kingdom
probabilistic methods
computational methods
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Wei Chen
University of Pittsburgh United States of America
statistical methods
computational methods
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Amir Afghari
Delft University of Technology Netherlands
probabilistic methods
statistical econometric methods
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Rui Duan
Harvard T.H. Chan School of Public Health United States of America
statistical methods
computational methods
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