Simulation-based inference toolkit
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Updated
May 17, 2024 - Python
Simulation-based inference toolkit
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
A system for scientific simulation-based inference at scale.
Conduct simulation-based inference on strong gravitational lensing systems.
Likelihood-free AMortized Posterior Estimation with PyTorch
SBI Workshop jointly by Helmholtz AI + ML ⇌ Science Colaboratory
A Python toolkit for (simulation-based) inference and the mechanization of science.
Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.
A simulation-based Inference (SBI) library designed to perform analysis on a wide class of gravitational wave signals
Community-sourced list of papers and resources on neural simulation-based inference.
Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.
Neural Simulation-based Inference with GNN for Jeans Modeling
Code for the paper "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation".
Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration
Research code and results for the paper "Simulation-based inference for computational connectomics" (Boelts et al. 2023 PLoS CB, @janfb)
Example of a fully Bayesian forecast using evidence networks applied to 21-cm cosmology
Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference
Simulation-based (likelihood-free) inference customized for astronomical applications
Simulation-based inference in JAX
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