Software

An initial list of SBI-related software packages

SBI (python) - general purpose SBI framework
SBI Benchmarking (python) - for benchmarking
MadMiner - aimed at particle physics
swyft (python) the reference implementation of Truncated Marginal Neural Ratio Estimation (TMNRE), an efficient, simulation-based inference technique for complex data and expensive simulators
carl an early NN-based SBI package
lampe (python) is a flexible, amortized sbi library. The philosophy of LAMPE is to avoid obfuscation and expose all components so it is most useful for researchers.
pydelfi (python) with neural density estimators and adaptive acquisition of simulations.

Latest Posts

More with less Simulation-based inference enables accurate diffusion-weighted MRI with minimal acquisition time

MF Eggl, S De Santis - bioRxiv, 2024 - biorxiv.org

… c) In this work we utilize a simulation-based inference approach to fit DTI and DKI tensors. Here, an artificial neural network is trained with an initial prior belief of the …

Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference

S Griesemer, D Cao, Z Cui, C Osorio, Y Liu - The Thirty-eighth Annual … - openreview.net

… This has led to the rise of simulation-based inference (SBI), a class of machine learning-enabled techniques for approaching inverse problems with stochastic simulators. …

Neural Conformal Inference for Jump Diffusion Processes

X Wang, HJ Hyun - Available at SSRN 5008097 - papers.ssrn.com

… Our work aligns with the principles of simulation-based inference. The approximate … For a brief introduction and a more detailed history of simulation-based inference, we …

Compositional simulation-based inference for time series

M Gloeckler, S Toyota, K Fukumizu… - arXiv preprint arXiv …, 2024 - arxiv.org

… In this work, we propose a framework for efficient simulation-based inference for Markovian time series simulators. Unlike other neural SBI methods that require long …