smsharma/awesome-neural-sbi

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

Awesome Neural SBI

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
  • Code Packages and Benchmarks
  • Tutorials
  • Review Papers
  • Discovery and Links
• Papers: Methods
• Papers: Application
  • Cosmology, Astrophysics, and Astronomy
  • Particle Physics
  • Neuroscience and Cognitive Science
  • Health and Medicine
  • Other Domains
  • Application to Real Data

Software and Resources

Code Packages and Benchmarks

• sbi [Code] [Docs] [Paper]: General-purpose simulation-based inference toolkit.
• BayesFlow [Code] [Docs] [Paper]: Simulation-based inference framework with a focus on amortized Bayesian workflows.
• sbibm [Code] [Docs] [Paper]: Simulation-based inference benchmarking framework.
• swyft [Code] [Docs] [Paper]: 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] [Docs]: Simulation-based inference in Julia.
• lampe [Code] [Docs]: Likelihood-free AMortized Posterior Estimation with PyTorch.
• sbijax [Code]: Simulation-based inference in JAX.
• nbi [Code] [Docs] [Paper]: Neural Posterior Estimation (NPE) package with a focus on astronomical light curves and spectra.
• MadMiner [Code] [Docs] [Paper]: Machine learning–based inference toolkit for particle physics.
• pydelfi [Code] [Docs] [Paper]: Early implementation of Density Estimation Likelihood-Free Inference (DELFI) with neural density estimators and adaptive acquisition of simulations.
• carl [Code] [Docs] [Paper]: Early toolbox for neural network-based likelihood-free inference in Python.

Tutorials

• SBI Tutorial: A hands-on tutorial introducing basic SBI concepts and methods.

Review Papers

• Neural Methods for Amortised Parameter Inference [arXiv]
  Andrew Zammit-Mangion, Matthew Sainsbury-Dale, Raphaël Huser

• The frontier of simulation-based inference [arXiv]
  Kyle Cranmer, Johann Brehmer, Gilles Louppe

Discovery and Links

• arXiv search for "simulation-based inference" or "likelihood-free inference"
• Google Scholar search for "simulation-based inference" or "likelihood-free inference"
• simulation-based-inference.org: Community resource on simulation-based inference, including an automatically-compiled list of 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]
  Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

• Addressing Misspecification in Simulation-based Inference through Data-driven Calibration [arXiv]
  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]
  Xiaoyu Wang, Ryan P. Kelly, David J. Warne, Christopher Drovandi

• All-in-one simulation-based inference [arXiv]
  Manuel Gloeckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H. Macke

• Diffusion posterior sampling for simulation-based inference in tall data settings [arXiv]
  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]
  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]
  Luca Masserano, Alex Shen, Michele Doro, Tommaso Dorigo, Rafael Izbicki, Ann B. Lee

• Simulation-Based Inference with Quantile Regression [arXiv]
  He Jia

• Consistency Models for Scalable and Fast Simulation-Based Inference [arXiv]
  Marvin Schmitt, Valentin Pratz, Ullrich Köthe, Paul-Christian Bürkner, Stefan T Radev

• Pseudo-Likelihood Inference [arXiv]
  Theo Gruner, Boris Belousov, Fabio Muratore, Daniel Palenicek, Jan Peters

• Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference [arXiv]
  Marvin Schmitt, Stefan T. Radev, Paul-Christian Bürkner

• Direct Amortized Likelihood Ratio Estimation [arXiv]
  Adam D. Cobb, Brian Matejek, Daniel Elenius, Anirban Roy, Susmit Jha

• Simulation based stacking [arXiv]
  Yuling Yao, Bruno Régaldo-Saint Blancard, Justin Domke

• Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability [arXiv]
  Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis

• Sensitivity-Aware Amortized Bayesian Inference [arXiv]
  Lasse Elsemüller, Hans Olischläger, Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

• Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference [arXiv]
  Marvin Schmitt, Daniel Habermann, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

• Simulation-based Inference with the Generalized Kullback-Leibler Divergence [arXiv]
  Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré

• Data assimilation as simulation-based inference [Master thesis]
  Gérôme Andry, Gilles Louppe

• A transport approach to sequential simulation-based inference [arXiv]
  Paul-Baptiste Rubio, Youssef Marzouk, Matthew Parno

• Kernel-Based Tests for Likelihood-Free Hypothesis Testing [arXiv]
  Patrik Róbert Gerber, Tianze Jiang, Yury Polyanskiy, Rui Sun

• Scalable inference with Autoregressive Neural Ratio Estimation [arXiv]
  Noemi Anau Montel, James Alvey, Christoph Weniger

• Simulation-based inference using surjective sequential neural likelihood estimation [arXiv]
  Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz

• Hierarchical Neural Simulation-Based Inference Over Event Ensembles [arXiv]
  Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer

• L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference [arXiv]
  Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues

• Flow Matching for Scalable Simulation-Based Inference [arXiv]
  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]
  Daolang Huang, Ayush Bharti, Amauri Souza, Luigi Acerbi, Samuel Kaski

• Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation [arXiv]
  Richard Gao, Michael Deistler, Jakob H. Macke

• Simultaneous identification of models and parameters of scientific simulators [arXiv]
  Cornelius Schröder, Jakob H. Macke

• Discriminative calibration [arXiv]
  Yuling Yao, Justin Domke

• Variational Inference with Coverage Guarantees [arXiv]
  Yash Patel, Declan McNamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari

• Disentangled Multi-Fidelity Deep Bayesian Active Learning [arXiv]
  Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, Rose Yu

• Balancing Simulation-based Inference for Conservative Posteriors [arXiv]
  Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe

• JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models [arXiv]
  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]
  Pablo Lemos, Adam Coogan, Yashar Hezaveh, Laurence Perreault-Levasseur

• Misspecification-robust Sequential Neural Likelihood [arXiv]
  Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Chris Drovandi

• A Deep Learning Method for Comparing Bayesian Hierarchical Models [arXiv]
  Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner, Stefan T. Radev

• Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive Models [arXiv]
  Lukas Schumacher, Paul-Christian Bürkner, Andreas Voss, Ullrich Köthe, Stefan T. Radev

• Validation Diagnostics for SBI algorithms based on Normalizing Flows [arXiv]
  Julia Linhart, Alexandre Gramfort, Pedro L. C. Rodrigues

• Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference [arXiv]
  Bingjie Wang, Joel Leja, Ashley Villar, Joshua S. Speagle

• Likelihood-free hypothesis testing [arXiv]
  Patrik Róbert Gerber, Yury Polyanskiy

• Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference [arXiv]
  Pierre Glaser, Michael Arbel, Arnaud Doucet, Arthur Gretton

• Efficient identification of informative features in simulation-based inference [arXiv]
  Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob Macke, Philipp Berens

• Robust Neural Posterior Estimation and Statistical Model Criticism [arXiv]
  Daniel Ward, Patrick Cannon, Mark Beaumont, Matteo Fasiolo, Sebastian M Schmon

• Contrastive Neural Ratio Estimation [arXiv]
  Benjamin Kurt Miller, Christoph Weniger, Patrick Forré

• Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models [arXiv]
  Louis Sharrock, Jack Simons, Song Liu, Mark Beaumont

• Truncated proposals for scalable and hassle-free simulation-based inference [arXiv]
  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]
  Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

• Compositional Score Modeling for Simulation-based Inference [arXiv]
  Tomas Geffner, George Papamakarios, Andriy Mnih

• Investigating the Impact of Model Misspecification in Neural Simulation-based Inference [arXiv]
  Patrick Cannon, Daniel Ward, Sebastian M. Schmon

• Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation [arXiv]
  Arnaud Delaunoy, Joeri Hermans, François Rozet, Antoine Wehenkel, Gilles Louppe

• Bayesian model comparison for simulation-based inference [arXiv]
  A. Spurio Mancini, M. M. Docherty, M. A. Price, J. D. McEwen

• Likelihood-Free Inference with Generative Neural Networks via Scoring Rule Minimization [arXiv]
  Lorenzo Pacchiardi, Ritabrata Dutta

• Simulation-Based Inference with Waldo: Confidence Regions by Leveraging Prediction Algorithms or Posterior Estimators for Inverse Problems [arXiv]
  Luca Masserano, Tommaso Dorigo, Rafael Izbicki, Mikael Kuusela, Ann B. Lee

• Learning Optimal Test Statistics in the Presence of Nuisance Parameters [arXiv]
  Lukas Heinrich

• GATSBI: Generative Adversarial Training for Simulation-Based Inference [arXiv]
  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]
  Manuel Glöckler, Michael Deistler, Jakob H. Macke

• Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap [arXiv]
  Charita Dellaporta, Jeremias Knoblauch, Theodoros Damoulas, François-Xavier Briol

• Flexible and efficient simulation-based inference for models of decision-making [bioRxiv]
  Jan Boelts, Jan-Matthis Lueckmann, Richard Gao, Jakob H. Macke

• Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks [arXiv]
  Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe, Stefan T. Radev

• Group equivariant neural posterior estimation [arXiv]
  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]
  Joeri Hermans, Arnaud Delaunoy, François Rozet, Antoine Wehenkel, Volodimir Begy, Gilles Louppe

• Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference [arXiv]
  François Rozet, Gilles Louppe

• Likelihood-Free Frequentist Inference: Confidence Sets with Correct Conditional Coverage [arXiv]
  Niccolò Dalmasso, Luca Masserano, David Zhao, Rafael Izbicki, Ann B. Lee

• Truncated Marginal Neural Ratio Estimation [arXiv] [Code]
  Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger

• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [arXiv]
  Giulio Isacchini, Natanael Spisak, Armita Nourmohammad, Thierry Mora, Aleksandra M. Walczak

• Simulation-Based Inference with Approximately Correct Parameters via Maximum Entropy [arXiv]
  Rainier Barrett, Mehrad Ansari, Gourab Ghoshal, Andrew D White

• Sequential Neural Posterior and Likelihood Approximation [arXiv]
  Samuel Wiqvist, Jes Frellsen, Umberto Picchini

• Diagnostics for Conditional Density Models and Bayesian Inference Algorithms [arXiv]
  David Zhao, Niccolò Dalmasso, Rafael Izbicki, Ann B. Lee

• HNPE: Leveraging Global Parameters for Neural Posterior Estimation [arXiv]
  Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort

• Benchmarking Simulation-Based Inference [arXiv]
  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]
  Niall Jeffrey, Benjamin D. Wandelt

• Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference [arXiv]
  Maxime Vandegar, Michael Kagan, Antoine Wehenkel, Gilles Louppe

• Neural Approximate Sufficient Statistics for Implicit Models [arXiv]
  Yanzhi Chen, Dinghuai Zhang, Michael Gutmann, Aaron Courville, Zhanxing Zhu

• Amortized Bayesian Model Comparison With Evidential Deep Learning [arXiv]
  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]
  Stefan T. Radev, Ulf K. Mertens, Andreass Voss, Lynton Ardizzone, Ullrich Köthe

• Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems [arXiv]
  Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

• On Contrastive Learning for Likelihood-free Inference [arXiv]
  Conor Durkan, Iain Murray, George Papamakarios

• Automatic Posterior Transformation for Likelihood-Free Inference [arXiv]
  David S. Greenberg, Marcel Nonnenmacher, Jakob H. Macke

• Likelihood-free MCMC with Amortized Approximate Ratio Estimators [arXiv]
  Joeri Hermans, Volodimir Begy, Gilles Louppe

• Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) [arXiv]
  Traiko Dinev, Michael U. Gutmann

• Analyzing Inverse Problems with Invertible Neural Networks [arXiv]
  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]
  Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

• Mining gold from implicit models to improve likelihood-free inference [arXiv] [Code]
  Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

• Likelihood-free inference with emulator networks [arXiv]
  Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke

• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows [arXiv] [Code]
  George Papamakarios, David C. Sterratt, Iain Murray

• A Guide to Constraining Effective Field Theories with Machine Learning [arXiv]
  Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

• Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation [arXiv]
  George Papamakarios, Iain Murray

• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers [arXiv]
  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]
  Christopher Wilson, Rachel Bean

• Simulation-based Inference for Gravitational-waves from Intermediate-Mass Binary Black Holes in Real Noise [arXiv]
  Vivien Raymond, Sama Al-Shammari, Alexandre Göttel

• Efficient Massive Black Hole Binary parameter estimation for LISA using Sequential Neural Likelihood [arXiv]
  Iván Martín Vílchez, Carlos F. Sopuerta

• Simulation-based inference of radio millisecond pulsars in globular clusters [arXiv]
  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]
  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]
  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]
  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]
  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]
  Costantino Pacilio, Swetha Bhagwat, Roberto Cotesta

• How much information can be extracted from galaxy clustering at the field level? [arXiv]
  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]
  Konstantin Karchev, Matthew Grayling, Benjamin M. Boyd, Roberto Trotta, Kaisey S. Mandel, Christoph Weniger

• Tuning neural posterior estimation for gravitational wave inference [arXiv]
  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]
  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]
  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]
  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]
  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]
  Bradley Greig, David Prelogović, Yuxiang Qin, Yuan-Sen Ting, Andrei Mesinger

• Fast likelihood-free inference in the LSS Stage IV era [arXiv]
  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]
  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]
  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]
  Katharena Christy, Eric J. Baxter, Jason Kumar

• SIMBIG : Cosmological Constraints from the Redshift-Space Galaxy Skew Spectra [arXiv]
  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]
  Eric J. Baxter, Shivam Pandey

• Simulation-based inference of deep fields: galaxy population model and redshift distributions [arXiv]
  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]
  Didier Barret, Simon Dupourqué

• Optimal, fast, and robust inference of reionization-era cosmology with the 21cmPIE-INN [arXiv]
  Benedikt Schosser, Caroline Heneka, Tilman Plehn

• Isolated Pulsar Population Synthesis with Simulation-Based Inference [arXiv]
  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]
  Frederick B. Davies et al

• Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling [arXiv]
  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]
  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]
  Deep Chatterjee, Philip C. Harris, Maanas Goel, Malina Desai, Michael W. Coughlin, Erik Katsavounidis

• Learning Reionization History from Quasars with Simulation-Based Inference [arXiv]
  Huanqing Chen, Joshua Speagle, Keir K. Rogers

• Simulation Based Inference of BNS Kilonova Properties: A Case Study with AT2017gfo [arXiv]
  Phelipe A. Darc, Clecio R. Bom, Bernardo M. O. Fraga, Charlie D. Kilpatrick

• Bayesian Simulation-based Inference for Cosmological Initial Conditions [arXiv]
  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]
  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]
  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]
  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]
  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]
  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]
  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]
  ChangHoon Hahn, Connor Bottrell, Khee-Gan Lee

• EFTofLSS meets simulation-based inference: σ8 from biased tracers [arXiv]
  Beatriz Tucci, Fabian Schmidt

• Sensitivity Analysis of Simulation-Based Inference for Galaxy Clustering [arXiv]
  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]
  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]
  Mayeul Aubin et al

• Simulation-based inference for stochastic gravitational wave background data analysis [arXiv]
  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]
  James Alvey, Uddipta Bhardwaj, Samaya Nissanke, Christoph Weniger

• Neural Posterior Estimation with guaranteed exact coverage: the ringdown of GW150914 [arXiv]
  Marco Crisostomi, Kallol Dey, Enrico Barausse, Roberto Trotta

• The likelihood of the 21-cm power spectrum [arXiv]
  David Prelogović, Andrei Mesinger

• The angular power spectrum of gravitational-wave transient sources as a probe of the large-scale structure [arXiv]
  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]
  Bingjie Wang, Joel Leja, V. Ashley Villar, Joshua S. Speagle

• Peregrine: Sequential simulation-based inference for gravitational wave signals [arXiv]
  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]
  James Alvey, Mathis Gerdes, Christoph Weniger

• Investigating the turbulent hot gas in X-COP galaxy clusters [arXiv]
  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]
  Anchal Saxena, Alex Cole, Simon Gazagnes, P. Daniel Meerburg, Christoph Weniger, Samuel J. Witte

• Neural posterior estimation for exoplanetary atmospheric retrieval [arXiv]
  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]
  Samuel Gagnon-Hartman, John Ruan, Daryl Haggard

• Calibrating cosmological simulations with implicit likelihood inference using galaxy growth observables [arXiv]
  Yongseok Jo et al

• DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation [arXiv]
  Gourav Khullar, Brian Nord, Aleksandra Ciprijanovic, Jason Poh, Fei Xu

• Detection is truncation: studying source populations with truncated marginal neural ratio estimation [arXiv]
  Noemi Anau Montel, Christoph Weniger

• SIMBIG : A Forward Modeling Approach To Analyzing Galaxy Clustering [arXiv]
  ChangHoon Hahn et al

• Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference [arXiv]
  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]
  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]
  Konstantin Karchev, Roberto Trotta, Christoph Weniger

• Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation [arXiv]
  Gemma Zhang, Siddharth Mishra-Sharma, Cora Dvorkin

• Uncovering dark matter density profiles in dwarf galaxies with graph neural networks [arXiv]
  Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib

• Estimating Cosmological Constraints from Galaxy Cluster Abundance using Simulation-Based Inference [arXiv]
  Moonzarin Reza, Yuanyuan Zhang, Brian Nord, Jason Poh, Aleksandra Ciprijanovic, Louis Strigari

• Neural Posterior Estimation with Differentiable Simulators [arXiv]
  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]
  Androniki Dimitriou, Christoph Weniger, Camila A. Correa

• Estimating the warm dark matter mass from strong lensing images with truncated marginal neural ratio estimation [arXiv]
  Noemi Anau Montel, Adam Coogan, Camila Correa, Konstantin Karchev, Christoph Weniger

• Implicit Likelihood Inference of Reionization Parameters from the 21 cm Power Spectrum [arXiv]
  Xiaosheng Zhao, Yi Mao, Benjamin D. Wandelt

• Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation [arXiv]
  ChangHoon Hahn, Peter Melchior

• Simulation-Based Inference of Strong Gravitational Lensing Parameters [arXiv]
  Ronan Legin, Yashar Hezaveh, Laurence Perreault Levasseur, Benjamin Wandelt

• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation [arXiv]
  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]
  Siddharth Mishra-Sharma, Kyle Cranmer

• Inferring dark matter substructure with astrometric lensing beyond the power spectrum [arXiv]
  Siddharth Mishra-Sharma

• Approximate Bayesian Neural Doppler Imaging [arXiv]
  A. Asensio Ramos, C. Diaz Baso, O. Kochukhov

• Lossless, Scalable Implicit Likelihood Inference for Cosmological Fields [arXiv]
  T. Lucas Makinen, Tom Charnock, Justin Alsing, Benjamin D. Wandelt

• Real-time gravitational-wave science with neural posterior estimation [arXiv]
  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]
  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]
  Joeri Hermans, Nilanjan Banik, Christoph Weniger, Gianfranco Bertone, Gilles Louppe

• Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization [arXiv]
  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]
  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]
  Niall Jeffrey, Justin Alsing, François Lanusse

• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning [arXiv]
  Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer

• Fast likelihood-free cosmology with neural density estimators and active learning [arXiv]
  Justin Alsing, Tom Charnock, Stephen Feeney, Benjamin Wandelt

Particle Physics

• Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production [arXiv]
  Radha Mastandrea, Benjamin Nachman, Tilman Plehn

• Simulation-based inference in the search for CP violation in leptonic WH production [arXiv]
  Ricardo Barrué, Patricia Conde-Muíño, Valerio Dao, Rui Santos

• Reconstructing axion-like particles from beam dumps with simulation-based inference [arXiv]
  Alessandro Morandini, Torben Ferber, Felix Kahlhoefer

• Measuring QCD Splittings with Invertible Networks [arXiv]
  Sebastian Bieringer, Anja Butter, Theo Heimel, Stefan Höche, Ullrich Köthe, Tilman Plehn, Stefan T. Radev

• Simulation-based inference methods for particle physics [arXiv]
  Johann Brehmer, Kyle Cranmer

• MadMiner: Machine learning-based inference for particle physics [arXiv]
  Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

• Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale [arXiv]
  Atılım Güneş Baydin et al

• Constraining Effective Field Theories with Machine Learning [arXiv]
  Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

Neuroscience and Cognitive Science

• Approximation of Intractable Likelihood Functions in Systems Biology via Normalizing Flows [arXiv]
  Vincent D. Zaballa, Elliot E. Hui

• Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference [bioRxiv]
  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]
  Amin Ghaderi-Kangavari, Jamal Amani Rad, Michael D. Nunez

• Simulation-based Inference for Model Parameterization on Analog Neuromorphic Hardware [arXiv]
  Jakob Kaiser, Raphael Stock, Eric Müller, Johannes Schemmel, Sebastian Schmitt

• Simulation-based inference for efficient identification of generative models in computational connectomics [bioRxiv]
  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]
  Alexander Fengler, Lakshmi N Govindarajan, Tony Chen, Michael J Frank

• Training deep neural density estimators to identify mechanistic models of neural dynamics [Paper]
  Pedro J Gonçalves et al

• Mental speed is high until age 60 as revealed by analysis of over a million participants [Paper]
  Mischa von Krause, Stefan T. Radev, Andreas Voss

• Amortized Bayesian Inference for Models of Cognition [arXiv]
  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]
  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]
  Michael A. Ramirez-Sierra, Thomas R. Sokolowski

• Modeling the Age Pattern of Fertility: An Individual-Level Approach [arXiv]
  Daniel Ciganda, Nicolas Todd

• Simulation-based Inference for Cardiovascular Models [arXiv]
  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]
  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]
  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]
  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]
  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]
  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]
  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]
  Mila Gorecki, Jakob H. Macke, Michael Deistler

• Optimal simulation-based Bayesian decisions [arXiv]
  Justin Alsing, Thomas D. P. Edwards, Benjamin Wandelt

• Graph-informed simulation-based inference for models of active matter [arXiv]
  Namid R. Stillman, Silke Henkes, Roberto Mayor, Gilles Louppe

• Simulation-based inference of single-molecule force spectroscopy [arXiv]
  Lars Dingeldein, Pilar Cossio, Roberto Covino

• Normalizing flows for likelihood-free inference with fusion simulations [Paper]
  C S Furia, R M Churchill

• Amortized Bayesian Inference of GISAXS Data with Normalizing Flows [arXiv]
  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]
  Vincent Zaballa, Elliot Hui

• Simulation-based Bayesian inference for multi-fingered robotic grasping [arXiv]
  Norman Marlier, Olivier Brüls, Gilles Louppe

• Simulation-based inference of evolutionary parameters from adaptation dynamics using neural networks [bioRxiv]
  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]
  ChangHoon Hahn et al

• Mental speed is high until age 60 as revealed by analysis of over a million participants [Paper] Mischa von Krause, Stefan T. Radev, Andreas Voss

• A neural simulation-based inference approach for characterizing the Galactic Center γ-ray excess [arXiv]
  Siddharth Mishra-Sharma, Kyle Cranmer

• Towards constraining warm dark matter with stellar streams through neural simulation-based inference (Preliminary) [arXiv]
  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]
  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]
  Niall Jeffrey, Justin Alsing, François Lanusse