https://github.com/smsharma/awesome-neural-sbi

Community-sourced list of papers and resources on neural simulation-based inference.
https://github.com/smsharma/awesome-neural-sbi

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Community-sourced list of papers and resources on neural simulation-based inference.

• Host: GitHub
• URL: https://github.com/smsharma/awesome-neural-sbi
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• License: mit
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README

        


Awesome Neural SBI





[![License: MIT](https://img.shields.io/badge/License-MIT-red.svg)](https://opensource.org/licenses/MIT)

[![Pull requests welcome](https://img.shields.io/badge/Pull%20Requests-welcome-green.svg?logo=github)](https://github.com/smsharma/awesome-neural-sbi/pulls)



A community-sourced list of papers and resources on neural simulation-based inference, covering both methodological developments and domain applications. Given the nature of the field, the list is bound to be highly incomplete -- contributions are welcome!

# Contents

- [Software and Resources](#software-and-resources)

  - [Code Packages and Benchmarks](#code-packages-and-benchmarks)

  - [Tutorials](#tutorials)

  - [Review Papers](#review-papers)

  - [Discovery and Links](#discovery-and-links)

- [Papers: Methods](#papers-methods)

- [Papers: Application](#papers-application)

  - [Cosmology, Astrophysics, and Astronomy](#cosmology-astrophysics-and-astronomy)

  - [Particle Physics](#particle-physics)

  - [Neuroscience and Cognitive Science](#neuroscience-and-cognitive-science)

  - [Health and Medicine](#health-and-medicine)

  - [Other Domains](#other-domains)

  - [Application to Real Data](#application-to-real-data)

# Software and Resources

  

## Code Packages and Benchmarks

- `sbi` [[Code]](https://github.com/mackelab/sbi) [[Docs]](https://www.mackelab.org/sbi/) [[Paper]](https://joss.theoj.org/papers/10.21105/joss.02505): General-purpose simulation-based inference toolkit.

- `BayesFlow` [[Code]](https://github.com/stefanradev93/BayesFlow) [[Docs]](https://bayesflow.org/) [[Paper]](https://joss.theoj.org/papers/10.21105/joss.05702): Simulation-based inference framework with a focus on amortized Bayesian workflows.

- `sbibm` [[Code]](https://github.com/sbi-benchmark/sbibm) [[Docs]](https://sbi-benchmark.github.io/) [[Paper]](https://arxiv.org/abs/2312.03824): Simulation-based inference benchmarking framework.

- `swyft` [[Code]](https://github.com/undark-lab/swyft) [[Docs]](https://swyft.readthedocs.io/en/latest/) [[Paper]](https://joss.theoj.org/papers/10.21105/joss.04205): Official implementation of Truncated Marginal Neural Ratio Estimation (TMNRE), a hyper-efficient, simulation-based inference technique for complex data and expensive simulators. 

- `SimulationBasedInference.jl` [[Code]](https://github.com/bgroenks96/SimulationBasedInference.jl) [[Docs]](https://bgroenks96.github.io/SimulationBasedInference.jl/dev/): Simulation-based inference in Julia.

- `lampe` [[Code]](https://github.com/francois-rozet/lampe) [[Docs]](https://lampe.readthedocs.io): Likelihood-free AMortized Posterior Estimation with PyTorch.

- `sbijax` [[Code]](https://github.com/dirmeier/sbijax): Simulation-based inference in JAX.

- `nbi` [[Code]](https://github.com/kmzzhang/nbi) [[Docs]](https://nbi.readthedocs.io/en/latest/) [[Paper]](https://arxiv.org/abs/2312.03824): Neural Posterior Estimation (NPE) package with a focus on astronomical light curves and spectra.

- `MadMiner` [[Code]](https://github.com/madminer-tool/madminer) [[Docs]](https://madminer.readthedocs.io/en/latest/index.html) [[Paper]](https://arxiv.org/abs/1907.10621): Machine learning–based inference toolkit for particle physics.

- `pydelfi` [[Code]](https://github.com/justinalsing/pydelfi) [[Docs]](https://pydelfi.readthedocs.io/en/latest/intro.html) [[Paper]](https://arxiv.org/abs/1903.00007): Early implementation of Density Estimation Likelihood-Free Inference (DELFI) with neural density estimators and adaptive acquisition of simulations.

- `carl` [[Code]](https://github.com/diana-hep/carl) [[Docs]](http://diana-hep.org/carl/) [[Paper]](https://joss.theoj.org/papers/10.21105/joss.00011): Early toolbox for neural network-based likelihood-free inference in Python.

## Tutorials

- [SBI Tutorial](https://github.com/smsharma/sbi-lecture-mit): A hands-on tutorial introducing basic SBI concepts and methods.

## Review Papers

- **Neural Methods for Amortised Parameter Inference** [[arXiv]](https://arxiv.org/abs/2404.12484)  

  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser

- **The frontier of simulation-based inference** [[arXiv]](https://arxiv.org/abs/1911.01429)  

  Kyle Cranmer, Johann Brehmer, Gilles Louppe

## Discovery and Links

- [arXiv search](https://arxiv.org/search/advanced?advanced=1&terms-0-operator=AND&terms-0-term=%22simulation-based+inference%22+&terms-0-field=all&terms-1-operator=OR&terms-1-term=%22likelihood-free+inference%22&terms-1-field=all&classification-computer_science=y&classification-mathematics=y&classification-physics=y&classification-physics_archives=all&classification-q_biology=y&classification-statistics=y&classification-include_cross_list=include&date-filter_by=all_dates&date-year=&date-from_date=&date-to_date=&date-date_type=submitted_date&abstracts=show&size=50&order=-announced_date_first) for "simulation-based inference" or "likelihood-free inference"

- [Google Scholar search](https://scholar.google.com/scholar?hl=en&scisbd=1&as_sdt=0%2C22&q=%22simulation-based+inference%22+OR+%22likelihood-free+inference%22&btnG=) for "simulation-based inference" or "likelihood-free inference"

- [simulation-based-inference.org](http://simulation-based-inference.org/): Community resource on simulation-based inference, including an automatically-compiled [list of papers](http://simulation-based-inference.org/papers/).

# Papers: Methods

*Methodological and use-inspired papers. Listed in reverse-chronological order.*

- **Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation** [[arXiv]](https://arxiv.org/abs/2406.03154)  

  Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

- **Addressing Misspecification in Simulation-based Inference through Data-driven Calibration** [[arXiv]](https://arxiv.org/abs/2405.08719)  

  Antoine Wehenkel, Juan L. Gamella, Ozan Sener, Jens Behrmann, Guillermo Sapiro, Marco Cuturi, Jörn-Henrik Jacobsen

- **Preconditioned Neural Posterior Estimation for Likelihood-free Inference** [[arXiv]](https://arxiv.org/abs/2404.13557)  

  Xiaoyu Wang, Ryan P. Kelly, David J. Warne, Christopher Drovandi

- **All-in-one simulation-based inference** [[arXiv]](https://arxiv.org/abs/2404.09636)  

  Manuel Gloeckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H. Macke

- **Diffusion posterior sampling for simulation-based inference in tall data settings** [[arXiv]](https://arxiv.org/abs/2404.07593)  

  Julia Linhart, Gabriel Victorino Cardoso, Alexandre Gramfort, Sylvain Le Corff, Pedro L. C. Rodrigues

- **Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings** [[arXiv]](https://arxiv.org/abs/2403.07454)  

  Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini

- **Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference** [[arXiv]](https://arxiv.org/abs/2402.05330)  

Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee

- **Simulation-Based Inference with Quantile Regression** [[arXiv]](https://arxiv.org/abs/2401.02413)  

He Jia

- **Consistency Models for Scalable and Fast Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2312.05440)  

Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T Radev

- **Pseudo-Likelihood Inference** [[arXiv]](https://arxiv.org/abs/2311.16656)  

Theo Gruner, Boris Belousov, Fabio Muratore, Daniel Palenicek, Jan Peters

- **Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2311.10671)  

Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner

- **Direct Amortized Likelihood Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2311.10571)  

Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha

- **Simulation based stacking** [[arXiv]](https://arxiv.org/abs/2310.17009)  

Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke

- **Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability** [[arXiv]](https://arxiv.org/abs/2310.13402)  

Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis

- **Sensitivity-Aware Amortized Bayesian Inference** [[arXiv]](https://arxiv.org/abs/2310.11122)  

Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

- **Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference** [[arXiv]](https://arxiv.org/abs/2310.04395)  

Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

- **Simulation-based Inference with the Generalized Kullback-Leibler Divergence** [[arXiv]](https://arxiv.org/abs/2310.01808)  

Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré

- **Data assimilation as simulation-based inference** [[Master thesis]](https://matheo.uliege.be/handle/2268.2/18255)  

Gérôme Andry, Gilles Louppe

- **A transport approach to sequential simulation-based inference** [[arXiv]](https://arxiv.org/abs/2308.13940)  

Paul-Baptiste Rubio, Youssef Marzouk, Matthew Parno

- **Kernel-Based Tests for Likelihood-Free Hypothesis Testing** [[arXiv]](https://arxiv.org/abs/2308.09043)  

Patrik Róbert Gerber, Tianze Jiang, Yury Polyanskiy, Rui Sun

- **Scalable inference with Autoregressive Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2308.08597)  

Noemi Anau Montel, James Alvey, Christoph Weniger

- **Simulation-based inference using surjective sequential neural likelihood estimation** [[arXiv]](https://arxiv.org/abs/2308.01054)  

Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz

- **Hierarchical Neural Simulation-Based Inference Over Event Ensembles** [[arXiv]](https://arxiv.org/abs/2306.12584)  

Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer

- **L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2306.03580)  

Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues

- **Flow Matching for Scalable Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2305.17161)  

Maximilian Dax, Jonas Wildberger, Simon Buchholz, Stephen R. Green, Jakob H. Macke, Bernhard Schölkopf

- **Learning Robust Statistics for Simulation-based Inference under Model Misspecification** [[arXiv]](https://arxiv.org/abs/2305.15871)  

Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi, Samuel Kaski

- **Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation** [[arXiv]](https://arxiv.org/abs/2305.15208)  

Richard Gao, Michael Deistler, Jakob H. Macke

- **Simultaneous identification of models and parameters of scientific simulators** [[arXiv]](https://arxiv.org/abs/2305.15174)  

Cornelius Schröder, Jakob H. Macke

- **Discriminative calibration** [[arXiv]](https://arxiv.org/abs/2305.14593)  

Yuling Yao, Justin Domke

- **Variational Inference with Coverage Guarantees** [[arXiv]](https://arxiv.org/abs/2305.14275)  

Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

- **Disentangled Multi-Fidelity Deep Bayesian Active Learning** [[arXiv]](https://arxiv.org/abs/2305.04392)  

Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

- **Balancing Simulation-based Inference for Conservative Posteriors** [[arXiv]](https://arxiv.org/abs/2304.10978)  

Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe

- **JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models** [[arXiv]](https://arxiv.org/abs/2302.09125)  

Stefan T. Radev, Marvin Schmitt, Valentin Pratz, Umberto Picchini, Ullrich Köthe, Paul-Christian Bürkner

- **Sampling-Based Accuracy Testing of Posterior Estimators for General Inference** [[arXiv]](https://arxiv.org/abs/2302.03026)  

Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur

- **Misspecification-robust Sequential Neural Likelihood** [[arXiv]](https://arxiv.org/abs/2301.13368)  

Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Chris Drovandi

- **A Deep Learning Method for Comparing Bayesian Hierarchical Models** [[arXiv]](https://arxiv.org/abs/2301.11873)  

Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner, Stefan T. Radev

- **Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models** [[arXiv]](https://arxiv.org/abs/2211.13165)  

Lukas Schumacher, Paul-Christian Bürkner, Andreas Voss, Ullrich Köthe, Stefan T. Radev

- **Validation Diagnostics for SBI algorithms based on Normalizing Flows** [[arXiv]](https://arxiv.org/abs/2211.09602)  

Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues

- **Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference** [[arXiv]](https://arxiv.org/abs/2211.03747)  

Bingjie Wang, Joel Leja, Ashley Villar, Joshua S. Speagle

- **Likelihood-free hypothesis testing** [[arXiv]](https://arxiv.org/abs/2211.01126)  

Patrik Róbert Gerber, Yury Polyanskiy

- **Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2210.14756)  

Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton

- **Efficient identification of informative features in simulation-based inference** [[arXiv]](https://arxiv.org/abs/2210.11915)  

Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob Macke, Philipp Berens

- **Robust Neural Posterior Estimation and Statistical Model Criticism** [[arXiv]](https://arxiv.org/abs/2210.06564)  

Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian M Schmon

- **Contrastive Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2210.06170)  

Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

- **Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models** [[arXiv]](https://arxiv.org/abs/2210.04872)  

Louis Sharrock, Jack Simons, Song Liu, Mark Beaumont

- **Truncated proposals for scalable and hassle-free simulation-based inference** [[arXiv]](https://arxiv.org/abs/2210.04815)  

Michael Deistler, Pedro J Goncalves, Jakob H Macke

- **New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2210.01680)  

Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

- **Compositional Score Modeling for Simulation-based Inference** [[arXiv]](https://arxiv.org/abs/2209.14249)  

Tomas Geffner, George Papamakarios, Andriy Mnih

- **Investigating the Impact of Model Misspecification in Neural Simulation-based Inference** [[arXiv]](https://arxiv.org/abs/2209.01845)  

Patrick Cannon, Daniel Ward, Sebastian M. Schmon

- **Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2208.13624)  

Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe

- **Bayesian model comparison for simulation-based inference** [[arXiv]](https://arxiv.org/abs/2205.15784)  

A. Spurio Mancini, M. M. Docherty, M. A. Price, J. D. McEwen

- **Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization** [[arXiv]](https://arxiv.org/abs/2205.15784)  

Lorenzo Pacchiardi, Ritabrata Dutta

- **Simulation-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms or Posterior Estimators for Inverse Problems** [[arXiv]](https://arxiv.org/abs/2205.15680)  

Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee

- **Learning Optimal Test Statistics in the Presence of Nuisance Parameters** [[arXiv]](https://arxiv.org/abs/2203.13079)  

Lukas Heinrich

- **GATSBI: Generative Adversarial Training for Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2203.06481)  

Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke

- **Variational methods for simulation-based inference** [[arXiv]](https://arxiv.org/abs/2203.04176)  

Manuel Glöckler, Michael Deistler, Jakob H. Macke

- **Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap** [[arXiv]](https://arxiv.org/abs/2202.04744)  

Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol

- **Flexible and efficient simulation-based inference for models of decision-making** [[bioRxiv]](https://www.biorxiv.org/content/10.1101/2021.12.22.473472v3)  

Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, Jakob H. Macke

- **Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks** [[arXiv]](https://arxiv.org/abs/2112.08866)  

Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

- **Group equivariant neural posterior estimation** [[arXiv]](https://arxiv.org/abs/2111.13139)  

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke

- **A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful** [[arXiv]](https://arxiv.org/abs/2110.06581)  

Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe

- **Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference** [[arXiv]](https://arxiv.org/abs/2110.00449)  

François Rozet, Gilles Louppe

- **Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage** [[arXiv]](https://arxiv.org/abs/2107.03920)  

Niccolò Dalmasso, Luca Masserano, David Zhao, Rafael Izbicki, Ann B. Lee

- **Truncated Marginal Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2107.01214)  [[Code]](https://github.com/undark-lab/swyft/)  

Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger

- **MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories** [[arXiv]](https://arxiv.org/abs/2106.01808)  

Giulio Isacchini, Natanael Spisak, Armita Nourmohammad, Thierry Mora, Aleksandra M. Walczak

- **Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy** [[arXiv]](https://arxiv.org/abs/2104.09668)  

Rainier Barrett, Mehrad Ansari, Gourab Ghoshal, Andrew D White

- **Sequential Neural Posterior and Likelihood Approximation** [[arXiv]](https://arxiv.org/abs/2102.06522)  

Samuel Wiqvist, Jes Frellsen, Umberto Picchini

- **Diagnostics for Conditional Density Models and Bayesian Inference Algorithms** [[arXiv]](https://arxiv.org/abs/2102.10473)  

David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee

- **HNPE: Leveraging Global Parameters for Neural Posterior Estimation** [[arXiv]](https://arxiv.org/abs/2102.06477)  

Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort

- **Benchmarking Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2101.04653)  

Jan-Matthis Lueckmann, Jan Boelts, David S. Greenberg, Pedro J. Gonçalves, Jakob H. Macke

- **Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks** [[arXiv]](https://arxiv.org/abs/2011.05991)  

Niall Jeffrey, Benjamin D. Wandelt

- **Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2011.05836)  

Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe

- **Neural Approximate Sufficient Statistics for Implicit Models** [[arXiv]](https://arxiv.org/abs/2010.10079)  

Yanzhi Chen, Dinghuai Zhang, Michael Gutmann, Aaron Courville, Zhanxing Zhu

- **Amortized Bayesian Model Comparison With Evidential Deep Learning** [[arXiv]](https://arxiv.org/abs/2004.10629)  

Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens, Andreas Voss, Ullrich Köthe, Paul-Christian Bürkner

- **BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks** [[arXiv]](https://arxiv.org/abs/2003.06281)  

Stefan T. Radev, Ulf K. Mertens, Andreass Voss, Lynton Ardizzone, Ullrich Köthe

- **Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems** [[arXiv]](https://arxiv.org/abs/2002.09301)  

Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

- **On Contrastive Learning for Likelihood-free Inference** [[arXiv]](https://arxiv.org/abs/2002.03712)  

Conor Durkan, Iain Murray, George Papamakarios

- **Automatic Posterior Transformation for Likelihood-Free Inference** [[arXiv]](https://arxiv.org/abs/1905.07488)  

David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke

- **Likelihood-free MCMC with Amortized Approximate Ratio Estimators** [[arXiv]](https://arxiv.org/abs/1903.04057)  

Joeri Hermans, Volodimir Begy, Gilles Louppe

- **Dynamic Likelihood-free Inference via Ratio Estimation (DIRE)** [[arXiv]](https://arxiv.org/abs/1810.09899)  

Traiko Dinev, Michael U. Gutmann

- **Analyzing Inverse Problems with Invertible Neural Networks** [[arXiv]](https://arxiv.org/abs/1808.04730)  

Lynton Ardizzone, Jakob Kruse, Sebastian Wirkert, Daniel Rahner, Eric W. Pellegrini, Ralf S. Klessen, Lena Maier-Hein, Carsten Rother, Ullrich Köthe

- **Likelihood-free inference with an improved cross-entropy estimator** [[arXiv]](https://arxiv.org/abs/1808.00973)  

Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

- **Mining gold from implicit models to improve likelihood-free inference** [[arXiv]](https://arxiv.org/abs/1805.12244) [[Code]](https://github.com/johannbrehmer/simulator-mining-example)   

Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

- **Likelihood-free inference with emulator networks** [[arXiv]](https://arxiv.org/abs/1805.09294)  

Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke

- **Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows** [[arXiv]](https://arxiv.org/abs/1805.07226) [[Code]](https://github.com/gpapamak/snl)  

George Papamakarios, David C. Sterratt, Iain Murray

- **A Guide to Constraining Effective Field Theories with Machine Learning** [[arXiv]](https://arxiv.org/abs/1805.00020)  

Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

- **Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation** [[arXiv]](https://arxiv.org/abs/1605.06376)  

George Papamakarios, Iain Murray

- **Approximating Likelihood Ratios with Calibrated Discriminative Classifiers** [[arXiv]](https://arxiv.org/abs/1506.02169)  

Kyle Cranmer, Juan Pavez, Gilles Louppe

# Papers: Application

*Domain application of neural simulation-based inference. Papers listed in reverse-chronological order.*

## Cosmology, Astrophysics, and Astronomy

- **Fisher's Mirage: Noise Tightening of Cosmological Constraints in Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2406.06067)  

  Christopher Wilson, Rachel Bean

- **Simulation-based Inference for Gravitational-waves from Intermediate-Mass Binary Black Holes in Real Noise** [[arXiv]](https://arxiv.org/abs/2406.03935)  

  Vivien Raymond, Sama Al-Shammari, Alexandre Göttel

- **Efficient Massive Black Hole Binary parameter estimation for LISA using Sequential Neural Likelihood** [[arXiv]](https://arxiv.org/abs/2406.00565)  

  Iván Martín Vílchez, Carlos F. Sopuerta

- **Simulation-based inference of radio millisecond pulsars in globular clusters** [[arXiv]](https://arxiv.org/abs/2405.15691)  

  Joanna Berteaud, Christopher Eckner, Francesca Calore, Maïca Clavel, Daryl Haggard

- **Dark Energy Survey Year 3 results: simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps II. Cosmological results** [[arXiv]](https://arxiv.org/abs/2405.10881)  

  M. Gatti, G. Campailla, N. Jeffrey, L. Whiteway, A. Porredon, J. Prat, J. Williamson, M. Raveri, B. Jain, V. Ajani, C. Zhou, J. Blazek, D. Anbajagane, S. Samuroff, T. Kacprzak, A. Alarcon, A. Amon, K. Bechtol, M. Becker, G. Bernstein, A. Campos, C. Chang, R. Chen

- **A Parameter-Masked Mock Data Challenge for Beyond-Two-Point Galaxy Clustering Statistics** [[arXiv]](https://arxiv.org/abs/2405.02252)  

  Beyond-2pt Collaboration, :, Elisabeth Krause, Yosuke Kobayashi, Andrés N. Salcedo, Mikhail M. Ivanov, Tom Abel, Kazuyuki Akitsu, Raul E. Angulo, Giovanni Cabass, Sofia Contarini, Carolina Cuesta-Lazaro, ChangHoon Hahn, Nico Hamaus, Donghui Jeong, Chirag Modi, Nhat-Minh Nguyen, Takahiro Nishimichi, Enrique Paillas, Marcos Pellejero Ibañez, Oliver H. E. Philcox, Alice Pisani, Fabian Schmidt, Satoshi Tanaka, Giovanni Verza

- **KiDS-SBI: Simulation-Based Inference Analysis of KiDS-1000 Cosmic Shear** [[arXiv]](https://arxiv.org/abs/2404.15402)  

  Maximilian von Wietersheim-Kramsta, Kiyam Lin, Nicolas Tessore, Benjamin Joachimi, Arthur Loureiro, Robert Reischke, Angus H. Wright

- **A Strong Gravitational Lens Is Worth a Thousand Dark Matter Halos: Inference on Small-Scale Structure Using Sequential Methods** [[arXiv]](https://arxiv.org/abs/2404.14487)  

  Sebastian Wagner-Carena, Jaehoon Lee, Jeffrey Pennington, Jelle Aalbers, Simon Birrer, Risa H. Wechsler

- **Simulation-based inference of black hole ringdowns in the time domain** [[arXiv]](https://arxiv.org/abs/2404.11373)  

  Costantino Pacilio, Swetha Bhagwat, Roberto Cotesta

- **How much information can be extracted from galaxy clustering at the field level?** [[arXiv]](https://arxiv.org/abs/2403.03220)  

  Nhat-Minh Nguyen, Fabian Schmidt, Beatriz Tucci, Martin Reinecke, Andrija Kostić

- **SIDE-real: Supernova Ia Dust Extinction with truncated marginal neural ratio estimation applied to real data** [[arXiv]](https://arxiv.org/abs/2403.07871)  

  Konstantin Karchev, Matthew Grayling, Benjamin M. Boyd, Roberto Trotta, Kaisey S. Mandel, Christoph Weniger

- **Tuning neural posterior estimation for gravitational wave inference** [[arXiv]](https://arxiv.org/abs/2403.02443)  

  Alex Kolmus, Justin Janquart, Tomasz Baka, Twan van Laarhoven, Chris Van Den Broeck, Tom Heskes

- **Dark Energy Survey Year 3 results: likelihood-free, simulation-based wCDM inference with neural compression of weak-lensing map statistics** [[arXiv]](https://arxiv.org/abs/2403.02314)  

  N. Jeffrey, L. Whiteway, M. Gatti, J. Williamson, J. Alsing, A. Porredon, J. Prat, C. Doux, B. Jain, C. Chang, T. -Y. Cheng, T. Kacprzak, P. Lemos, A. Alarcon, A. Amon, K. Bechtol, M. R. Becker, G. M. Bernstein, A. Campos, A. Carnero Rosell, R. Chen, A. Choi, J. DeRose, A. Drlica-Wagner, K. Eckert

- **Simulation-Based Inference of the sky-averaged 21-cm signal from CD-EoR with REACH** [[arXiv]](https://arxiv.org/abs/2403.14618)  

  Anchal Saxena, P. Daniel Meerburg, Christoph Weniger, Eloy de Lera Acedo, Will Handley

- **Exploring the role of the halo mass function for inferring astrophysical parameters during reionisation** [[arXiv]](https://arxiv.org/abs/2403.14061)  

  Bradley Greig, David Prelogović, Jordan Mirocha, Yuxiang Qin, Yuan-Sen Ting, Andrei Mesinger

- **SimBIG: Cosmological Constraints using Simulation-Based Inference of Galaxy Clustering with Marked Power Spectra** [[arXiv]](https://arxiv.org/abs/2404.04228)  

  Elena Massara, ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Jiamin Hou, Pablo Lemos, Chirag Modi, Azadeh Moradinezhad Dizgah, Liam Parker, Bruno Régaldo-Saint Blancard

- **Inferring astrophysical parameters using the 2D cylindrical power spectrum from reionisation** [[arXiv]](https://arxiv.org/abs/2403.14060)  

  Bradley Greig, David Prelogović, Yuxiang Qin, Yuan-Sen Ting, Andrei Mesinger

- **Fast likelihood-free inference in the LSS Stage IV era** [[arXiv]](https://arxiv.org/abs/2403.14750)  

  Guillermo Franco Abellán, Guadalupe Cañas Herrera, Matteo Martinelli, Oleg Savchenko, Davide Sciotti, Christoph Weniger

- **Simulation-based Bayesian inference of protoplanetary disk winds from forbidden line profiles** [[arXiv]](https://arxiv.org/abs/2403.10243)  

  Ahmad Nemer, ChangHoon Hahn, Jiaxuan Li, Peter Melchior, Jeremy Goodman

- **Neural Simulation-Based Inference of the Neutron Star Equation of State directly from Telescope Spectra** [[arXiv]](https://arxiv.org/abs/2403.00287)  

  Len Brandes, Chirag Modi, Aishik Ghosh, Delaney Farrell, Lee Lindblom, Lukas Heinrich, Andrew W. Steiner, Fridolin Weber, Daniel Whiteson

- **Applying Simulation-Based Inference to Spectral and Spatial Information from the Galactic Center Gamma-Ray Excess** [[arXiv]](https://arxiv.org/abs/2402.04549)  

Katharena Christy, Eric J. Baxter, Jason Kumar

- **SIMBIG : Cosmological Constraints from the Redshift-Space Galaxy Skew Spectra** [[arXiv]](https://arxiv.org/abs/2401.15074)  

Jiamin Hou, Azadeh Moradinezhad Dizgah, ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Pablo Lemos, Elena Massara, Chirag Modi, Liam Parker, Bruno Régaldo-Saint Blancard

- **Inferring galaxy cluster masses from cosmic microwave background lensing with neural simulation based inference** [[arXiv]](https://arxiv.org/abs/2401.08910)  

Eric J. Baxter, Shivam Pandey

- **Simulation-based inference of deep fields: galaxy population model and redshift distributions** [[arXiv]](https://arxiv.org/abs/2401.06846)  

Beatrice Moser, Tomasz Kacprzak, Silvan Fischbacher, Alexandre Refregier, Dominic Grimm, Luca Tortorelli

- **Simulation-Based Inference with Neural Posterior Estimation applied to X-ray spectral fitting: Demonstration of working principles down to the Poisson regime** [[arXiv]](https://arxiv.org/abs/2401.06061)  

Didier Barret, Simon Dupourqué

- **Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN** [[arXiv]](https://arxiv.org/abs/2401.04174)  

Benedikt Schosser, Caroline Heneka, Tilman Plehn

- **Isolated Pulsar Population Synthesis with Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2312.14848)  

Vanessa Graber, Michele Ronchi, Celsa Pardo-Araujo, Nanda Rea

- **Constraints on the Evolution of the Ionizing Background and Ionizing Photon Mean Free Path at the End of Reionization** [[arXiv]](https://arxiv.org/abs/2312.08464)  

Frederick B. Davies et al

- **Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling** [[arXiv]](https://arxiv.org/abs/2312.08295)  

Timothy D. Gebhard, Jonas Wildberger, Maximilian Dax, Daniel Angerhausen, Sascha P. Quanz, Bernhard Schölkopf

- **Efficient Parameter Inference for Gravitational Wave Signals in the Presence of Transient Noises Using Normalizing Flow** [[arXiv]](https://arxiv.org/abs/2312.08122)  

Tian-Yang Sun, Chun-Yu Xiong, Shang-Jie Jin, Yu-Xin Wang, Jing-Fei Zhang, Xin Zhang

- **Optimizing Likelihood-Free Inference using Self-Supervised Neural Symmetry Embeddings** [[arXiv]](https://arxiv.org/abs/2312.07615)  

Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai, Michael W. Coughlin, Erik Katsavounidis

- **Learning Reionization History from Quasars with Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2311.16238)  

Huanqing Chen, Joshua Speagle, Keir K. Rogers

- **Simulation Based Inference of BNS Kilonova Properties: A Case Study with AT2017gfo** [[arXiv]](https://arxiv.org/abs/2311.09471)  

Phelipe A. Darc, Clecio R. Bom, Bernardo M. O. Fraga, Charlie D. Kilpatrick

- **Bayesian Simulation-based Inference for Cosmological Initial Conditions** [[arXiv]](https://arxiv.org/abs/2310.19910)  

Florian List, Noemi Anau Montel, Christoph Weniger

- **Simulation-based Inference of Reionization Parameters from 3D Tomographic 21 cm Light-cone Images -- II: Application of Solid Harmonic Wavelet Scattering Transform** [[arXiv]](https://arxiv.org/abs/2310.17602)  

Xiaosheng Zhao, Yi Mao, Shifan Zuo, Benjamin D. Wandelt

- **Dark Energy Survey Year 3 results: simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps I: validation on simulations** [[arXiv]](https://arxiv.org/abs/2310.17557)  

M. Gatti, N. Jeffrey, L. Whiteway, J. Williamson, B. Jain, V. Ajani, D. Anbajagane, G. Giannini, C. Zhou, A. Porredon, J. Prat, M. Yamamoto, J. Blazek, T. Kacprzak, S. Samuroff, A. Alarcon, A. Amon, K. Bechtol, M. Becker, G. Bernstein, A. Campos, C. Chang, R. Chen, A. Choi, C. Davis , et al.

- **SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering** [[arXiv]](https://arxiv.org/abs/2310.15256)  

Pablo Lemos, Liam Parker, ChangHoon Hahn, Shirley Ho, Michael Eickenberg, Jiamin Hou, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Bruno Regaldo-Saint Blancard, David Spergel

- **SIMBIG: Galaxy Clustering Analysis with the Wavelet Scattering Transform** [[arXiv]](https://arxiv.org/abs/2310.15250)  

Bruno Régaldo-Saint Blancard, ChangHoon Hahn, Shirley Ho, Jiamin Hou, Pablo Lemos, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Liam Parker, Yuling Yao, Michael Eickenberg

- **SIMBIG: The First Cosmological Constraints from the Non-Linear Galaxy Bispectrum** [[arXiv]](https://arxiv.org/abs/2310.15243)  

ChangHoon Hahn, Michael Eickenberg, Shirley Ho, Jiamin Hou, Pablo Lemos, Elena Massara, Chirag Modi, Azadeh Moradinezhad Dizgah, Liam Parker, Bruno Régaldo-Saint Blancard

- **Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects** [[arXiv]](https://arxiv.org/abs/2310.15234)  

Natalí S. M. de Santi, Francisco Villaescusa-Navarro, L. Raul Abramo, Helen Shao, Lucia A. Perez, Tiago Castro, Yueying Ni, Christopher C. Lovell, Elena Hernandez-Martinez, Federico Marinacci, David N. Spergel, Klaus Dolag, Lars Hernquist, Mark Vogelsberger

- **HaloFlow I: Neural Inference of Halo Mass from Galaxy Photometry and Morphology** [[arXiv]](https://arxiv.org/abs/2310.04503)  

ChangHoon Hahn, Connor Bottrell, Khee-Gan Lee

- **EFTofLSS meets simulation-based inference: σ8 from biased tracers** [[arXiv]](https://arxiv.org/abs/2310.03741)  

Beatriz Tucci, Fabian Schmidt

- **Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering** [[arXiv]](https://arxiv.org/abs/2309.15071)  

Chirag Modi, Shivam Pandey, Matthew Ho, ChangHoon Hahn, Bruno Regaldo-Saint Blancard, Benjamin Wandelt

- **Hybrid SBI or How I Learned to Stop Worrying and Learn the Likelihood** [[arXiv]](https://arxiv.org/abs/2309.10270)  

Chirag Modi, Oliver H. E. Philcox

- **Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows** [[arXiv]](https://arxiv.org/abs/2309.07954)  

Mayeul Aubin et al

- **Simulation-based inference for stochastic gravitational wave background data analysis** [[arXiv]](https://arxiv.org/abs/2309.09337)  

 James Alvey, Uddipta Bhardwaj, Valerie Domcke, Mauro Pieroni, Christoph Weniger

- **What to do when things get crowded? Scalable joint analysis of overlapping gravitational wave signals** [[arXiv]](https://arxiv.org/abs/2308.06318)  

James Alvey, Uddipta Bhardwaj, Samaya Nissanke, Christoph Weniger

- **Neural Posterior Estimation with guaranteed exact coverage: the ringdown of GW150914** [[arXiv]](https://arxiv.org/abs/2305.18528)  

Marco Crisostomi, Kallol Dey, Enrico Barausse, Roberto Trotta

- **The likelihood of the 21-cm power spectrum** [[arXiv]](https://arxiv.org/abs/2305.03074)  

David Prelogović, Andrei Mesinger

- **The angular power spectrum of gravitational-wave transient sources as a probe of the large-scale structure** [[arXiv]](https://arxiv.org/abs/2305.02652)  

Yanyan Zheng, Nikolaos Kouvatsos, Jacob Golomb, Marco Cavaglià, Arianna I. Renzini, Mairi Sakellariadou

- **SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Application** [[arXiv]](https://arxiv.org/abs/2304.05281)  

Bingjie Wang, Joel Leja, V. Ashley Villar, Joshua S. Speagle

- **Peregrine: Sequential simulation-based inference for gravitational wave signals** [[arXiv]](https://arxiv.org/abs/2304.02035)  

Uddipta Bhardwaj, James Alvey, Benjamin Kurt Miller, Samaya Nissanke, Christoph Weniger

- **Albatross: A scalable simulation-based inference pipeline for analysing stellar streams in the Milky Way** [[arXiv]](https://arxiv.org/abs/2304.02032)  

James Alvey, Mathis Gerdes, Christoph Weniger

- **Investigating the turbulent hot gas in X-COP galaxy clusters** [[arXiv]](https://arxiv.org/abs/2303.15102)  

Simon Dupourqué, Nicolas Clerc, Etienne Pointecouteau, Dominique Eckert, Stefano Ettori, Franco Vazza

- **Constraining the X-ray heating and reionization using 21-cm power spectra with Marginal Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2303.07339)  

Anchal Saxena, Alex Cole, Simon Gazagnes, P. Daniel Meerburg, Christoph Weniger, Samuel J. Witte

- **Neural posterior estimation for exoplanetary atmospheric retrieval** [[arXiv]](https://arxiv.org/abs/2301.06575)  

Malavika Vasist, François Rozet, Olivier Absil, Paul Mollière, Evert Nasedkin, Gilles Louppe

- **Debiasing Standard Siren Inference of the Hubble Constant with Marginal Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2301.05241)  

Samuel Gagnon-Hartman, John Ruan, Daryl Haggard

- **Calibrating cosmological simulations with implicit likelihood inference using galaxy growth observables** [[arXiv]](https://arxiv.org/abs/2211.16461)  

Yongseok Jo et al

- **DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation** [[arXiv]](https://arxiv.org/abs/2211.09126)  

Gourav Khullar, Brian Nord, Aleksandra Ciprijanovic, Jason Poh, Fei Xu

- **Detection is truncation: studying source populations with truncated marginal neural ratio estimation** [[arXiv]](https://arxiv.org/abs/2211.04291)  

Noemi Anau Montel, Christoph Weniger

- **SIMBIG : A Forward Modeling Approach To Analyzing Galaxy Clustering** [[arXiv]](https://arxiv.org/abs/2211.00723)  

ChangHoon Hahn et al

- **Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference** [[arXiv]](https://arxiv.org/abs/2210.05686)  

Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Pürrer, Jonas Wildberger, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

- **One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses** [[arXiv]](https://arxiv.org/abs/2209.09918)  

Adam Coogan, Noemi Anau Montel, Konstantin Karchev, Meiert W. Grootes, Francesco Nattino, Christoph Weniger

- **SICRET: Supernova Ia Cosmology with truncated marginal neural Ratio EsTimation** [[arXiv]](https://arxiv.org/abs/2209.06733)  

Konstantin Karchev, Roberto Trotta, Christoph Weniger

- **Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation** [[arXiv]](https://arxiv.org/abs/2208.13796)  

Gemma Zhang, Siddharth Mishra-Sharma, Cora Dvorkin

- **Uncovering dark matter density profiles in dwarf galaxies with graph neural networks** [[arXiv]](https://arxiv.org/abs/2208.12825)  

Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib

- **Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference** [[arXiv]](https://arxiv.org/abs/2208.00134)  

Moonzarin Reza, Yuanyuan Zhang, Brian Nord, Jason Poh, Aleksandra Ciprijanovic, Louis Strigari

- **Neural Posterior Estimation with Differentiable Simulators** [[arXiv]](https://arxiv.org/abs/2207.05636)  

Justine Zeghal, François Lanusse, Alexandre Boucaud, Benjamin Remy, Eric Aubourg

- **Towards reconstructing the halo clustering and halo mass function of N-body simulations using neural ratio estimation** [[arXiv]](https://arxiv.org/abs/2206.11312)  

Androniki Dimitriou, Christoph Weniger, Camila A. Correa

- **Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation** [[arXiv]](https://arxiv.org/abs/2205.09126)  

Noemi Anau Montel, Adam Coogan, Camila Correa, Konstantin Karchev, Christoph Weniger

- **Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum** [[arXiv]](https://arxiv.org/abs/2203.15734)  

Xiaosheng Zhao, Yi Mao, Benjamin D. Wandelt

- **Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation** [[arXiv]](https://arxiv.org/abs/2203.07391)  

ChangHoon Hahn, Peter Melchior

- **Simulation-Based Inference of Strong Gravitational Lensing Parameters** [[arXiv]](https://arxiv.org/abs/2112.05278)  

Ronan Legin, Yashar Hezaveh, Laurence Perreault Levasseur, Benjamin Wandelt

- **Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation** [[arXiv]](https://arxiv.org/abs/2111.08030)  

Alex Cole, Benjamin Kurt Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, Christoph Weniger

- **A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess** [[arXiv]](https://arxiv.org/abs/2110.06931)  

Siddharth Mishra-Sharma, Kyle Cranmer

- **Inferring dark matter substructure with astrometric lensing beyond the power spectrum** [[arXiv]](https://arxiv.org/abs/2110.01620)  

Siddharth Mishra-Sharma

- **Approximate Bayesian Neural Doppler Imaging** [[arXiv]](https://arxiv.org/abs/2108.09266)  

A. Asensio Ramos, C. Diaz Baso, O. Kochukhov

- **Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields** [[arXiv]](https://arxiv.org/abs/2107.07405)  

T. Lucas Makinen, Tom Charnock, Justin Alsing, Benjamin D. Wandelt

- **Real-time gravitational-wave science with neural posterior estimation** [[arXiv]](https://arxiv.org/abs/2106.12594)  

Maximilian Dax, Stephen R. Green, Jonathan Gair, Jakob H. Macke, Alessandra Buonanno, Bernhard Schölkopf

- **Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation** [[arXiv]](https://arxiv.org/abs/2102.05673)  

Keming Zhang, Joshua S. Bloom, B. Scott Gaudi, Francois Lanusse, Casey Lam, Jessica R. Lu

- **Towards constraining warm dark matter with stellar streams through neural simulation-based inference** [[arXiv]](https://arxiv.org/abs/2011.14923)  

Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, Gilles Louppe

- **Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization** [[arXiv]](https://arxiv.org/abs/2010.12931)  

Arnaud Delaunoy, Antoine Wehenkel, Tanja Hinderer, Samaya Nissanke, Christoph Weniger, Andrew R. Williamson, Gilles Louppe

- **The sum of the masses of the Milky Way and M31: a likelihood-free inference approach** [[arXiv]](https://arxiv.org/abs/2010.08537)  

Pablo Lemos, Niall Jeffrey, Lorne Whiteway, Ofer Lahav, Niam I Libeskind, Yehuda Hoffman

- **Likelihood-free inference with neural compression of DES SV weak lensing map statistics** [[arXiv]](https://arxiv.org/abs/2009.08459)  

Niall Jeffrey, Justin Alsing, François Lanusse

- **Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning** [[arXiv]](https://arxiv.org/abs/1909.02005)  

Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer

- **Fast likelihood-free cosmology with neural density estimators and active learning** [[arXiv]](https://arxiv.org/abs/1903.00007)  

Justin Alsing, Tom Charnock, Stephen Feeney, Benjamin Wandelt

## Particle Physics

- **Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production** [[arXiv]](https://arxiv.org/abs/2405.15847)  

  Radha Mastandrea, Benjamin Nachman, Tilman Plehn

- **Simulation-based inference in the search for CP violation in leptonic WH production** [[arXiv]](https://arxiv.org/abs/2308.02882)  

Ricardo Barrué, Patricia Conde-Muíño, Valerio Dao, Rui Santos

- **Reconstructing axion-like particles from beam dumps with simulation-based inference** [[arXiv]](https://arxiv.org/abs/2308.01353)  

Alessandro Morandini, Torben Ferber, Felix Kahlhoefer

- **Measuring QCD Splittings with Invertible Networks** [[arXiv]](https://arxiv.org/abs/2012.09873)  

Sebastian Bieringer, Anja Butter, Theo Heimel, Stefan Höche, Ullrich Köthe, Tilman Plehn, Stefan T. Radev

- **Simulation-based inference methods for particle physics** [[arXiv]](https://arxiv.org/abs/2010.06439)  

Johann Brehmer, Kyle Cranmer

- **MadMiner: Machine learning-based inference for particle physics** [[arXiv]](https://arxiv.org/abs/1907.10621)  

Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

- **Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale** [[arXiv]](https://arxiv.org/abs/1907.03382)  

Atılım Güneş Baydin et al

- **Constraining Effective Field Theories with Machine Learning** [[arXiv]](https://arxiv.org/abs/1805.00013)  

Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

## Neuroscience and Cognitive Science

- **Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows** [[arXiv]](https://arxiv.org/abs/2312.02391)  

Vincent D. Zaballa, Elliot E. Hui

- **Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference** [[bioRxiv]](https://www.biorxiv.org/content/10.1101/2023.04.17.537118v1.abstract)  

Nicholas Tolley, Pedro L. C. Rodrigues, Alexandre Gramfort, Stephanie Jones

- **A General Integrative Neurocognitive Modeling Framework to Jointly Describe EEG and Decision-making on Single Trials** [[Paper]](https://link.springer.com/article/10.1007/s42113-023-00167-4)  

Amin Ghaderi-Kangavari, Jamal Amani Rad, Michael D. Nunez

- **Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware** [[arXiv]](https://arxiv.org/abs/2303.16056)  

Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt

- **Simulation-based inference for efficient identification of generative models in computational connectomics** [[bioRxiv]](https://www.biorxiv.org/content/10.1101/2023.01.31.526269v1.abstract)  

Jan Boelts, Philipp Harth, Richard Gao, Daniel Udvary, Felipe Yáñez, Daniel Baum, Hans-Christian Hege, Marcel Oberlaender, Jakob H. Macke

- **Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience** [[Paper]](https://elifesciences.org/articles/65074)  

Alexander Fengler, Lakshmi N Govindarajan, Tony Chen, Michael J Frank

- **Training deep neural density estimators to identify mechanistic models of neural dynamics** [[Paper]](https://elifesciences.org/articles/56261)  

Pedro J Gonçalves et al

- **Mental speed is high until age 60 as revealed by analysis of over a million participants** [[Paper]](https://www.nature.com/articles/s41562-021-01282-7)  

Mischa von Krause, Stefan T. Radev, Andreas Voss 

- **Amortized Bayesian Inference for Models of Cognition** [[arXiv]](https://arxiv.org/abs/2005.03899)  

Stefan T. Radev, Andreas Voss, Eva Marie Wieschen, Paul-Christian Bürkner

## Health and Medicine

- **Simulation-Based Inference of Developmental EEG Maturation with the Spectral Graph Model** [[arXiv]](https://arxiv.org/abs/2405.02524)  

  Danilo Bernardo, Xihe Xie, Parul Verma, Jonathan Kim, Virginia Liu, Ye Wu, Pew-Thian Yap, Srikantan Nagarajan, Ashish Raj

- **AI-powered simulation-based inference of a genuinely spatial-stochastic model of early mouse embryogenesis** [[arXiv]](https://arxiv.org/abs/2402.15330)  

  Michael A. Ramirez-Sierra, Thomas R. Sokolowski

- **Modeling the Age Pattern of Fertility: An Individual-Level Approach** [[arXiv]](https://arxiv.org/abs/2312.08185)  

Daniel Ciganda, Nicolas Todd

- **Simulation-based Inference for Cardiovascular Models** [[arXiv]](https://arxiv.org/abs/2307.13918)    

Antoine Wehenkel, Jens Behrmann, Andrew C. Miller, Guillermo Sapiro, Ozan Sener, Marco Cuturi, Jörn-Henrik Jacobsen

- **Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation** [[bioRxiv]](https://www.biorxiv.org/content/10.1101/2023.01.09.523230v1.abstract)  

Itamar Caspi, Moran Meir, Nadav Ben Nun, Uri Yakhini, Adi Stern,  Yoav Ram

- **Simulation-Based Inference for Whole-Brain Network Modeling of Epilepsy using Deep Neural Density Estimators** [[medRxiv]](https://www.medrxiv.org/content/10.1101/2022.06.02.22275860v1)  

Meysam Hashemi, Anirudh N. Vattikonda, Jayant Jha, Viktor Sip, Marmaduke M. Woodman, Fabrice Bartolomei, Viktor K. Jirsa

- **OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany** [[arXiv]](https://arxiv.org/abs/2010.00300)  

Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa M. Eichel, Till Bärnighausen, Ullrich Köthe

- **Simulation-Based Inference for Global Health Decisions** [[arXiv]](https://arxiv.org/abs/2005.07062)  

Christian Schroeder de Witt et al

## Other Domains

*Applications where multiple papers could not be grouped under a single heading.*

- **SB-ETAS: using simulation based inference for scalable, likelihood-free inference for the ETAS model of earthquake occurrences** [[arXiv]](https://arxiv.org/abs/2404.16590)  

  Samuel Stockman, Daniel J. Lawson, Maximilian J. Werner

- **Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers** [[arXiv]](https://arxiv.org/abs/2312.02997)  

Guy Moss, Vjeran Višnjević, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews

- **Amortized Bayesian Decision Making for simulation-based models** [[arXiv]](https://arxiv.org/abs/2312.02674)  

Mila Gorecki, Jakob H. Macke, Michael Deistler

- **Optimal simulation-based Bayesian decisions** [[arXiv]](https://arxiv.org/abs/2311.05742)  

Justin Alsing, Thomas D. P. Edwards, Benjamin Wandelt

- **Graph-informed simulation-based inference for models of active matter** [[arXiv]](https://arxiv.org/abs/2304.06806)  

Namid R. Stillman, Silke Henkes, Roberto Mayor, Gilles Louppe

- **Simulation-based inference of single-molecule force spectroscopy** [[arXiv]](https://arxiv.org/abs/2209.10392)  

Lars Dingeldein, Pilar Cossio, Roberto Covino

- **Normalizing flows for likelihood-free inference with fusion simulations** [[Paper]](https://iopscience.iop.org/article/10.1088/1361-6587/ac828d)  

C S Furia, R M Churchill

- **Amortized Bayesian Inference of GISAXS Data with Normalizing Flows** [[arXiv]](https://arxiv.org/abs/2210.01543)  

Maksim Zhdanov, Lisa Randolph, Thomas Kluge, Motoaki Nakatsutsumi, Christian Gutt, Marina Ganeva, Nico Hoffmann

- **Optimal Design of Experiments for Simulation-Based Inference of Mechanistic Acyclic Biological Networks** [[arXiv]](https://arxiv.org/abs/2111.13612)  

Vincent Zaballa, Elliot Hui

- **Simulation-based Bayesian inference for multi-fingered robotic grasping** [[arXiv]](https://arxiv.org/abs/2109.14275)  

Norman Marlier, Olivier Brüls, Gilles Louppe

- **Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks** [[bioRxiv]](https://www.biorxiv.org/content/10.1101/2021.09.30.462581v1.abstract)  

Grace Avecilla, Julie N. Chuong, Fangfei Li, Gavin Sherlock, David Gresham, Yoav Ram

## Application to Real Data

*Applications of neural simulation-based inference beyond synthetic data.*

- **SimBIG : A Forward Modeling Approach To Analyzing Galaxy Clustering** [[arXiv]](https://arxiv.org/abs/2211.00723)  

ChangHoon Hahn et al

- **Mental speed is high until age 60 as revealed by analysis of over a million participants** [[Paper]](https://www.nature.com/articles/s41562-021-01282-7)

Mischa von Krause, Stefan T. Radev, Andreas Voss 

- **A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess** [[arXiv]](https://arxiv.org/abs/2110.06931)  

Siddharth Mishra-Sharma, Kyle Cranmer

- **Towards constraining warm dark matter with stellar streams through neural simulation-based inference** *(Preliminary)* [[arXiv]](https://arxiv.org/abs/2011.14923)  

Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, Gilles Louppe

- **OutbreakFlow: Model-based Bayesian inference of disease outbreak dynamics with invertible neural networks and its application to the COVID-19 pandemics in Germany** [[arXiv]](https://arxiv.org/abs/2010.00300)  

Stefan T. Radev, Frederik Graw, Simiao Chen, Nico T. Mutters, Vanessa M. Eichel, Till Bärnighausen, Ullrich Köthe

- **Likelihood-free inference with neural compression of DES SV weak lensing map statistics** [[arXiv]](https://arxiv.org/abs/2009.08459)  

Niall Jeffrey, Justin Alsing, François Lanusse