Simulation-based inference toolkit
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Updated
May 17, 2024 - Python
Simulation-based inference toolkit
distributed, likelihood-free inference
A system for scientific simulation-based inference at scale.
Roundtrip: density estimation with deep generative neural networks
Likelihood-free AMortized Posterior Estimation with PyTorch
Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.
Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.
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.
Community-sourced list of papers and resources on neural simulation-based inference.
Code for the paper "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation".
Code for "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods" (arxiv:2305.04634)
Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference
Simulator Expansion for Likelihood-Free Inference (SELFI): a python implementation
PyTorch implementation of inference aware neural optimisation (de Castro and Dorigo, 2018 https://www.sciencedirect.com/science/article/pii/S0010465519301948)
Approximate Bayesian Computation
Detection is truncation: studying source populations with truncated marginal neural ratio estimation. Code repository associated with https://arxiv.org/abs/2211.04291.
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
Simulator of the Lotka-Volterra prey-predator system with demographic and observational noise and biases
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