Simulation-based inference toolkit
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Updated
Jun 21, 2024 - Python
Simulation-based inference toolkit
distributed, likelihood-free inference
Roundtrip: density estimation with deep generative neural networks
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Likelihood-free AMortized Posterior Estimation with PyTorch
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"
Comparison of summary statistic selection methods with a unifying perspective.
Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference
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
pyLFI is a Python toolbox using likelihood-free inference (LFI) methods for estimating the posterior distributions of model parameters.
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
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
Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.
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