Simulation-based inference toolkit

distributed, likelihood-free inference

Likelihood-free AMortized Posterior Estimation with PyTorch

Roundtrip: density estimation with deep generative neural networks

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.

A simulation model for the digital reconstruction of 3D root system architectures. Integrated with a simulation-based inference generative deep learning model.

A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)

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"

Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference

pyLFI is a Python toolbox using likelihood-free inference (LFI) methods for estimating the posterior distributions of model parameters.

Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129

My framework to perform likelihood-free inference with toy models or real-life simulation

Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912

Comparison of summary statistic selection methods with a unifying perspective.

Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html

Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379