Simulation-based inference


Simulators are the modern manifestation of scientific theories. They implement mechanistic models of the underlying natural phenomena of interest as well as models for the instruments used to observe those phenomena. The expressiveness of programming languages facilitates the development of complex, high-fidelity simulations and the power of modern computing provides the ability to generate synthetic data from them. The flexibility of simulators has made them critical research tools (and major cyberinfrastructure investments) for predicting how systems will behave across many areas of science and engineering. Unfortunately, despite their predictive power, these simulators are poorly suited for statistical inference, which is a core aspect of data-intensive science. To meet this challenge, there are an emerging set of techniques for simulation-based inference (SBI).

Simulation-based inference is the next step in the methodological evolution of statistical practice in the sciences. SBI provides qualitatively new capabilities that can transform scientific practice in fields as diverse as evolutionary biology, systems biology, neuroscience, gravitational wave astronomy, dark matter astrophysics, cosmology, and particle physics. Inference problems in these areas are challenging because they involve high-dimensional, richly-structured spaces. Empowering domain scientists with the ability to directly infer from data the properties of the underlying mechanistic models that they are developing would be transformative.

SBI has also proven to be an effective lingua franca that facilitates communication between domain scientists and methodological experts, supports convergence research, and accelerates cross-pollination of ideas between fields.

Selected Resources


The plan is to turn this page into a crowd-sourced community resource that can collect recent papers including methodological developments and applications. Here are some links to get started:


Curated Awesome List

  • Awesome Neural SBI - A similar effort with a less automated, more human-curated list of SBI papers initiated by Siddharth Mishra-Sharma.


Furthermore, genome-scale metabolic models (GEMs) can be used to simulate disparate types of ‘omics data, even though the explicit calculation of likelihoods may be intractable. Thus, the use of “simulation-based” Bayesian inference approaches is a promising route for informing GEM structure and parameters from data [198]. However, scaling Bayesian approaches up to deal with the large space of possible GEM reconstructions is an open, exciting and challenging research direction.

Other curated resources list

Latest Posts

X Du, J Crodelle, VJ Barranca, S Li, Y Shi, S Gao… - bioRxiv, 2024 -

… To determine the free parameters of the model, we develop a simulation-based inference method with parallel sampling that aligns the model with the muscle cells’ …

D Lanzieri, J Zeghal, TL Makinen, A Boucaud… - arXiv preprint arXiv …, 2024 -

… case at the cost of requiring a Simulation-Based Inference approach. In both cases, … , at fixed neural architecture and Simulation-Based Inference pipeline. To achieve this, …

MA Ramirez-Sierra, TR Sokolowski - arXiv preprint arXiv:2407.10938, 2024 -

… Recently, modern deep-learning techniques have been integrated with simulation-based inference, creating an exciting new approach for estimating parameters of such …

M Hashemi, A Ziaeemehr, MM Woodman… - Machine Learning …, 2024 -

… Simulation-based inference 207 The key challenge to perform an efficient Bayesian … Simulation-based Inference (SBI) 216 or likelihood-free inference performs efficient …