Fetching the paper…
Reading the bibliography…
Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from model-simulations.
A contribution to the mathematical theory of epidemics
William Ogilvy Kermack and Anderson G McKendrick · 1927
Earlier work this paper cites.
A quantitative description of membrane current and its application to conduction and excitation in nerve
Alan L Hodgkin and Andrew F Huxley · 1952
Earlier work this paper cites.
Lotka-volterra population models
Peter J Wangersky · 1978
Earlier work this paper cites.
Using the sir algorithm to simulate posterior distributions
Donald B Rubin · 1988
Earlier work this paper cites.
A note on importance sampling using standardized weights
Augustine Kong · 1992
Earlier work this paper cites.
Sequential Monte Carlo methods in practice , volume 1
Arnaud Doucet, Nando De Freitas, Neil James Gordon, et al · 2001
Earlier work this paper cites.
Approximate bayesian computation in population genetics
Mark A Beaumont, Wenyang Zhang, and David J Balding · 2002
Earlier work this paper cites.
Particle filtering
Petar M Djuric, Jayesh H Kotecha, Jianqui Zhang, Yufei Huang, Tadesse Ghirmai, Mónica F Bugallo, and Joaquin Miguez · 2003
Earlier work this paper cites.
Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons
Astrid A Prinz, Cyrus P Billimoria, and Eve Marder · 2003
Earlier work this paper cites.
Similar network activity from disparate circuit parameters
Astrid A Prinz, Dirk Bucher, and Eve Marder · 2004
Earlier work this paper cites.
Nested sampling
John Skilling · 2004
Earlier work this paper cites.
Pattern recognition and machine learning , volume 4
Christopher M Bishop and Nasser M Nasrabadi · 2006
Earlier work this paper cites.
Validation of software for bayesian models using posterior quantiles
Samantha R Cook, Andrew Gelman, and Donald B Rubin · 2006
Earlier work this paper cites.
Adaptive multilevel splitting for rare event analysis
Frédéric Cérou and Arnaud Guyader · 2007
Earlier work this paper cites.
A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data
Shaul Druckmann, Yoav Banitt, Albert A Gidon, Felix Schürmann, Henry Markram, and Idan Segev · 2007
Earlier work this paper cites.
Adaptive approximate bayesian computation
M A Beaumont, J Cornuet, J Marin, and C P Robert · 2009
Earlier work this paper cites.
Non-linear regression models for approximate bayesian computation
Michael GB Blum and Olivier François · 2010
Earlier work this paper cites.
Statistical inference for noisy nonlinear ecological dynamic systems
Simon N Wood · 2010
Earlier work this paper cites.
Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties
Etay Hay, Sean Hill, Felix Schürmann, Henry Markram, and Idan Segev · 2011
Earlier work this paper cites.
Multiple models to capture the variability in biological neurons and networks
Eve Marder and Adam L Taylor · 2011
Cited alongside, same era.
Diagnostic tools for approximate bayesian computation using the coverage property
Dennis Prangle, Michael GB Blum, G Popovic, and SA Sisson · 2014
Cited alongside, same era.
Reconstruction and simulation of neocortical microcircuitry
Henry Markram, Eilif Muller, Srikanth Ramaswamy, Michael W Reimann, Marwan Abdellah, Carlos Aguado Sanchez, Anastasia Ailamaki, Lidia Alonso-Nanclares, Nicolas Antille, Selim Arsever, et al · 2015
Cited alongside, same era.
The neocortical microcircuit collaboration portal: a resource for rat somatosensory cortex
Srikanth Ramaswamy, Jean-Denis Courcol, Marwan Abdellah, Stanislaw R Adaszewski, Nicolas Antille, Selim Arsever, Guy Atenekeng, Ahmet Bilgili, Yury Brukau, Athanassia Chalimourda, et al · 2015
Cited alongside, same era.
Inference networks for sequential monte carlo in graphical models
Brooks Paige and Frank Wood · 2016
Cited alongside, same era.
Conditional density estimation tools in python and r with applications to photometric redshifts and likelihood-free cosmological inference
Niccolò Dalmasso, Taylor Pospisil, Ann B Lee, Rafael Izbicki, Peter E Freeman, and Alex I Malz · 2020
Later among the works it cites.
On contrastive learning for likelihood-free inference
Conor Durkan, Iain Murray, and George Papamakarios · 2020
Later among the works it cites.
Training deep neural density estimators to identify mechanistic models of neural dynamics
Pedro J Gonçalves, Jan-Matthis Lueckmann, Michael Deistler, Marcel Nonnenmacher, Kaan Öcal, Giacomo Bassetto, Chaitanya Chintaluri, William F Podlaski, Sara A Haddad, Tim P Vogels, et al · 2020
Later among the works it cites.
Likelihood-free mcmc with amortized approximate ratio estimators
Joeri Hermans, Volodimir Begy, and Gilles Louppe · 2020
Later among the works it cites.
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
Benjamin Kurt Miller, Alex Cole, Gilles Louppe, and Christoph Weniger · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fast ε \varepsilon -free inference of simulation models with bayesian conditional density estimation
George Papamakarios and Iain Murray · 2016
Cited alongside, same era.
Bluepyopt: leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience
Werner Van Geit, Michael Gevaert, Giuseppe Chindemi, Christian Rössert, Jean-Denis Courcol, Eilif B Muller, Felix Schürmann, Idan Segev, and Henry Markram · 2016
Cited alongside, same era.
Stan: A probabilistic programming language
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell · 2017
Cited alongside, same era.
Flexible statistical inference for mechanistic models of neural dynamics
Jan-Matthis Lueckmann, Pedro J Goncalves, Giacomo Bassetto, Kaan Öcal, Marcel Nonnenmacher, and Jakob H Macke · 2017
Cited alongside, same era.
Effective sample size for importance sampling based on discrepancy measures
Luca Martino, Víctor Elvira, and Francisco Louzada · 2017
Cited alongside, same era.
Validating bayesian inference algorithms with simulation-based calibration
Sean Talts, Michael Betancourt, Daniel Simpson, Aki Vehtari, and Andrew Gelman · 2018
Cited alongside, same era.
Neural spline flows
Conor Durkan, Artur Bekasov, Iain Murray, and George Papamakarios · 2019
Cited alongside, same era.
Later among the works it cites.
Bayesflow: Learning complex stochastic models with invertible neural networks
Stefan T Radev, Ulf K Mertens, Andreas Voss, Lynton Ardizzone, and Ullrich Köthe · 2020
Later among the works it cites.
sbi: A toolkit for simulation-based inference
Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, and Jakob H. Macke · 2020
Later among the works it cites.
Real-time gravitational wave science with neural posterior estimation
Maximilian Dax, Stephen R Green, Jonathan Gair, Jakob H Macke, Alessandra Buonanno, and Bernhard Schölkopf · 2021
Later among the works it cites.
Energy efficient network activity from disparate circuit parameters
Michael Deistler, Jakob H Macke, and Pedro J Gonçalves · 2021
Later among the works it cites.
Recordings from the c. borealis stomatogastric nervous system at different temperatures in the decentralized condition
Sara Ann Haddad and Eve Marder · 2021
Later among the works it cites.
Averting a crisis in simulation-based inference
Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, and Gilles Louppe · 2021
Later among the works it cites.
Benchmarking simulation-based inference
Jan-Matthis Lueckmann, Jan Boelts, David Greenberg, Pedro Goncalves, and Jakob Macke · 2021
Later among the works it cites.
Truncated marginal neural ratio estimation
Benjamin Miller, Alex Cole, Patrick Forré, Gilles Louppe, and Christoph Weniger · 2021
Later among the works it cites.
Arbitrary marginal neural ratio estimation for likelihood-free inference
François Rozet et al · 2021
Later among the works it cites.
Group equivariant neural posterior estimation
Maximilian Dax, Stephen R Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, and Jakob H. Macke · 2022
Closest in time.
Variational methods for simulation-based inference
Manuel Glöckler, Michael Deistler, and Jakob H. Macke · 2022
Closest in time.
GATSBI: Generative adversarial training for simulation-based inference
Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Goncalves, and Jakob H. Macke · 2022
Closest in time.
Likelihood-free inference by ratio estimation
Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, and Michael U Gutmann · 2022
Closest in time.