Simulation-based inference toolkit

A system for scientific simulation-based inference at scale.

Likelihood-free AMortized Posterior Estimation with PyTorch

R package for statistical inference using partially observed Markov processes

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

Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead

A Python toolkit for (simulation-based) inference and the mechanization of science.

Automatically convert Julia methods to Gen functions.

Simulation-based (likelihood-free) inference customized for astronomical applications

Simulation-based inference in JAX

Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration

SBI Workshop jointly by Helmholtz AI ML ⇌ Science Colaboratory

Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.

(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination

Julia package for neural estimation

Conduct simulation-based inference on strong gravitational lensing systems.

Hierarchical neural implicit inference over event ensembles. Code repository associated with https://arxiv.org/abs/2306.12584.

Code for the paper "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation".

A simulation-based Inference (SBI) library designed to perform analysis on a wide class of gravitational wave signals