Approximate Inference Professors in Global

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Rina Foygel Barber
University of Chicago United States of America
statistical inference
shape-constrained inference
statistical estimation
distribution-free inference
prediction
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Rajesh Ranganath
New York University United States of America
probabilistic inference
statistical inference
causal inference
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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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Xinran Li
University of Chicago United States of America
randomization-based inference
causal inference
bayesian inference
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Alastair Hall
University of Manchester United Kingdom
statistical inference
indirect inference
generalized empirical likelihood
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Ying Jin
University of Pennsylvania United States of America
selective inference
causal inference
distribution-free inference
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Cosma Shalizi
Carnegie Mellon University United States of America
statistical inference
causal inference
nonparametric prediction
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Vanessa Mcnealis
University of Glasgow United Kingdom
causal inference
bayesian inference
semi-parametric estimation
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Mohammad Salavatipour
University of Alberta Canada
approximation algorithms
hardness approximation
probabilistic randomized algorithms
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Raaz Dwivedi
Cornell University Netherlands
causal inference
bayesian inference
random sampling
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Krzysztof Latuszynski
University of Warwick United Kingdom
exact inference
applied probability
probabilistic algorithms
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Melike Alper
University of Southampton United Kingdom
empirical likelihood inference
design inference
variance estimation
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Ivan Kryven
Utrecht University Netherlands
random approximation algorithms
parameter inference problems
stochastic analysis
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Oliver Ray
University of Bristol United Kingdom
scientific inference
abductive reasoning
inductive reasoning
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Jan Johannes
Heidelberg University Germany
adaptive estimation
nonparametric and semiparametric inference
nonparametric bayesian inference
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Thomas Icard
Stanford University United States of America
causal inference
probabilistic reasoning
automated reasoning
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Mingyuan Zhou
University of Texas at Austin United States of America
approximate inference
probabilistic machine learning
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Venkat Chandrasekaran
California Institute of Technology United States of America
approximation theory
statistical inference
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Umberto Picchini
Chalmers University of Technology Sweden
approximate bayesian computation
statistical inference
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Umberto Picchini
University of Gothenburg Sweden
approximate bayesian computation
statistical inference
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