sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.
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sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.
distributed, likelihood-free inference
A system for scientific simulation-based inference at scale.
Community-sourced list of papers and resources on neural simulation-based inference.
Likelihood-free AMortized Posterior Estimation with PyTorch
Roundtrip: density estimation with deep generative neural networks
Julia package for simulation-based, likelihood-free parameter inference using neural networks.
Lectures on Bayesian statistics and information theory
Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.
Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Code for the paper "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation".
Flexible SED fitting using Synthesizer, powered by Simulation Based Inference
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
Likelihood-Free Inference for Julia.
Code for "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods" (arxiv:2305.04634)
Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.
Correlation functions versus field-level inference in cosmology: example with log-normal fields
PyTorch implementation of inference aware neural optimisation (de Castro and Dorigo, 2018 https://www.sciencedirect.com/science/article/pii/S0010465519301948)
A simulation model for the digital reconstruction of 3D root system architectures. Integrated with a simulation-based inference generative deep learning model.
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