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

SBI Workshop jointly by Helmholtz AI + ML ⇌ Science Colaboratory

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

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

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

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".

Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.