Approximate Inference Methodologies Professors in Global

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Rajesh Ranganath
New York University United States of America
probabilistic inference
statistical inference
causal inference
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Haziq Jamil
London School of Economics and Political Science United Kingdom
approximate bayesian methods
scalable estimation methods
statistical methods
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Dae Ham
University of Michigan United States of America
adaptive sequential inference
randomization inference
causal inference
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Abhishek Chakrabortty
Texas A&M University United States of America
statistical inference
causal inference
bayesian inference
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Alastair Hall
University of Manchester United Kingdom
statistical inference
indirect inference
moment-based estimation methods
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Xinran Li
University of Chicago United States of America
randomization-based inference
causal inference
bayesian inference
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Daniela Golinelli
University of Pennsylvania United States of America
statistical inference procedures
causal inference
quantitative methods
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Daniel Malinsky
Columbia University United States of America
semiparametric inference
causal inference
statistical methods
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Daniel Xiang
University of Chicago United States of America
selective inference
sparse-approximation signal detection
empirical bayes methodology
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Bruno Castanho Silva
Freie Universität Berlin Germany
empirical methods
causal inferences
quantitative methods
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Jan Johannes
Heidelberg University Germany
adaptive estimation
nonparametric and semiparametric inference
nonparametric bayesian inference
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Corwin Zigler
Brown University United States of America
causal inference
quantitative methodology
bayesian methodology
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Tom Rainforth
University of Oxford United Kingdom
approximate inference
probabilistic data-efficient approaches
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Heather Wilber
University of Washington United States of America
approximation theory
nonlinear approximation methods
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Armin Iske
University of Hamburg Germany
kernel-based approximation methods
numerical approximation
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Umberto Picchini
Chalmers University of Technology Sweden
approximate bayesian computation
statistical inference
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Mohammad Salavatipour
University of Alberta Canada
approximation algorithms
probabilistic randomized algorithms
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Umberto Picchini
University of Gothenburg Sweden
approximate bayesian computation
statistical inference
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Venkat Chandrasekaran
California Institute of Technology United States of America
approximation theory
statistical inference
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Mario Ventresca
Purdue University United States of America
approximation algorithms
automated design inference
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Massimiliano Tamborrino
University of Warwick United Kingdom
approximate bayesian computation
statistical inference
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