Simulation-based inference toolkit
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Updated
Aug 20, 2024 - Python
Simulation-based inference toolkit
distributed, likelihood-free inference
A system for scientific simulation-based inference at scale.
Likelihood-free AMortized Posterior Estimation with PyTorch
Community-sourced list of papers and resources on neural simulation-based inference.
Roundtrip: density estimation with deep generative neural networks
Lectures on Bayesian statistics and information theory
Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.
Julia package for neural estimation
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.
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Likelihood-Free Inference for Julia.
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)
Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
Simulator of the Lotka-Volterra prey-predator system with demographic and observational noise and biases
This is an interactive app (run on local computer) to visualize neural likelihood surfaces from the paper "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods"
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