Likelihood Approximation Professors in Global

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Ivan Kryven
Utrecht University Netherlands
random approximation algorithms
probability
randomized algorithms
stochastic analysis
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Matthew Jenssen
King's College London United Kingdom
probability
probabilistic
discrete probability
random matrix theory
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Umberto Picchini
University of Gothenburg Sweden
approximate bayesian computation
stochastic modelling
likelihood-free methods
statistical inference
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Gennady Samorodnitsky
Cornell University Netherlands
probability theory
applied probability
machine learning
stochastic modeling
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Sigurd Assing
University of Warwick United Kingdom
probability theory
stochastic analysis
machine learning
random processes
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Rina Foygel Barber
University of Chicago United States of America
statistical estimation
shape-constrained inference
prediction
statistical inference
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A.c.c. Coolen
Radboud University Netherlands
random networks
machine learning
statistical inference
predictive analysis
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Hina Shaheen
University of Manitoba Canada
probability stochastic processes
bayesian inference
machine learning
statistical machine learning
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Mohammad Salavatipour
University of Alberta Canada
approximation algorithms
hardness approximation
probabilistic randomized algorithms
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Satish Thittamaranahalli
University of Southern California United States of America
approximation randomization
probabilistic reasoning
machine learning
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Armin Iske
University of Hamburg Germany
numerical approximation
kernel-based approximation methods
machine learning
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Monika Eisenmann
Lund University Sweden
numerical approximation
randomized algorithms
machine learning
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Tom Rainforth
University of Oxford United Kingdom
approximate inference
probabilistic programming
statistical machine learning
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Miklos Racz
Northwestern University United States of America
probability
applied probability
discrete probability
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Miklos Racz
Northwestern University United States of America
probability
applied probability
discrete probability
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Umberto Picchini
Chalmers University of Technology Sweden
approximate bayesian computation
stochastic modelling
statistical inference
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Thaleia Zariphopoulou
University of Texas at Austin United States of America
probability
machine learning
stochastic optimization
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Lukasz Szpruch
University of Edinburgh United Kingdom
probability
stochastic analysis
machine learning
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Krzysztof Latuszynski
University of Warwick United Kingdom
applied probability
probabilistic algorithms
stochastic simulation
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Nanwei Wang
University of New Brunswick Canada
composite likelihood
scalable likelihood methods
machine learning
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Yeganeh Alimohammadi
University of Southern California United States of America
probability
machine learning
statistical inference
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