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Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows for data analysis.
“Tensorflow: a system for large-scale machine learning.”
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving and Michael Isard · 2016
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“Probabilistic programming in Python using PyMC3”
John Salvatier, Thomas. Wiecki and Christopher Fonnesbeck · 2016
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“Tensorflow: a system for large-scale machine learning.”
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving and Michael Isard · 2016
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“Probabilistic programming in Python using PyMC3”
John Salvatier, Thomas. Wiecki and Christopher Fonnesbeck · 2016
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“TensorFlow Distributions”, 2017
Joshua. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman and Rif. Saurous · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser and Illia Polosukhin · 2017
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“TensorFlow Distributions”, 2017
Joshua. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman and Rif. Saurous · 2017
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“Attention is all you need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser and Illia Polosukhin · 2017
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“ELFI: Engine for Likelihood-Free Inference”
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael. Gutmann, Aki Vehtari, Jukka Corander and Samuel Kaski · 2018
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“Validating Bayesian inference algorithms with simulation-based calibration”
Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari and Andrew Gelman · 2018
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“ELFI: Engine for Likelihood-Free Inference”
Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael. Gutmann, Aki Vehtari, Jukka Corander and Samuel Kaski · 2018
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“Validating Bayesian inference algorithms with simulation-based calibration”
Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari and Andrew Gelman · 2018
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“Automatic posterior transformation for likelihood-free inference”
David Greenberg, Marcel Nonnenmacher and Jakob Macke · 2019
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“Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows”
George Papamakarios, David Sterratt and Iain Murray · 2019
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“Automatic posterior transformation for likelihood-free inference”
David Greenberg, Marcel Nonnenmacher and Jakob Macke · 2019
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“Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows”
George Papamakarios, David Sterratt and Iain Murray · 2019
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“A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task”
Marco D’Alessandro, Stefan Radev, Andreas Voss and Luigi Lombardi · 2020
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“Bayesian workflow”
Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner and Martin Modrák · 2020
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“Amortized Bayesian model comparison with evidential deep learning”
Stefan Radev, Marco D’Alessandro, Ulf Mertens, Andreas Voss, Ullrich Köthe and Paul-Christian Bürkner · 2020
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“BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks”
Stefan Radev, Ulf Mertens, A Voss, L Ardizzone and U Köthe · 2020
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“Jumping to conclusion? a Lévy flight model of decision making”
Eva Wieschen, Andreas Voss and Stefan Radev · 2020
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“A Bayesian brain model of adaptive behavior: an application to the Wisconsin Card Sorting Task”
Marco D’Alessandro, Stefan Radev, Andreas Voss and Luigi Lombardi · 2020
Earlier work this paper cites.
“Bayesian workflow”
Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Bürkner and Martin Modrák · 2020
Earlier work this paper cites.
“Amortized Bayesian model comparison with evidential deep learning”
Stefan Radev, Marco D’Alessandro, Ulf Mertens, Andreas Voss, Ullrich Köthe and Paul-Christian Bürkner · 2020
Earlier work this paper cites.
“BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks”
Stefan Radev, Ulf Mertens, A Voss, L Ardizzone and U Köthe · 2020
Earlier work this paper cites.
“Jumping to conclusion? a Lévy flight model of decision making”
Eva Wieschen, Andreas Voss and Stefan Radev · 2020
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“Measuring QCD splittings with invertible networks”
Sebastian Bieringer, Anja Butter, Theo Heimel, Stefan Höche, Ullrich Köthe, Tilman Plehn and Stefan Radev · 2021
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“Truncated marginal neural ratio estimation”
Benjamin Miller, Alex Cole, Patrick Forré, Gilles Louppe and Christoph Weniger · 2021
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“Normalizing Flows for Probabilistic Modeling and Inference”
George Papamakarios, Eric Nalisnick, Danilo Rezende, Shakir Mohamed and Balaji Lakshminarayanan · 2021
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“OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany”
Stefan Radev, Frederik Graw, Simiao Chen, Nico Mutters, Vanessa Eichel, Till Bärnighausen and Ullrich Köthe · 2021
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“Mental speed is high until age 60 as revealed by analysis of over a million participants”
Mischa von Krause, Stefan Radev and Andreas Voss · 2022
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“Flexible and efficient simulation-based inference for models of decision-making”
Jan Boelts, Jan-Matthis Lueckmann, Richard Gao and Jakob Macke · 2022
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Paul-Christian Bürkner, Maximilian Scholz and Stefan. Radev · 2022
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“A general integrative neurocognitive modeling framework to jointly describe EEG and decision-making on single trials”
Amin Ghaderi-Kangavari, Jamal Rad and Michael Nunez · 2022
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“Towards reliable parameter extraction in MEMS final module testing using bayesian inference”
Monika Heringhaus, Yi Zhang, André Zimmermann and Lars Mikelsons · 2022
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“Toward a principled Bayesian workflow in cognitive science.”
Daniel Schad, Michael Betancourt and Shravan Vasishth · 2021
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“Measuring QCD splittings with invertible networks”
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Benjamin Miller, Alex Cole, Patrick Forré, Gilles Louppe and Christoph Weniger · 2021
Cited alongside, same era.
“Normalizing Flows for Probabilistic Modeling and Inference”
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“Mental speed is high until age 60 as revealed by analysis of over a million participants”
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