My framework to perform likelihood-free inference with toy models or real-life simulation
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Updated
Nov 19, 2020 - Python
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
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
Comparison of summary statistic selection methods with a unifying perspective.
Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379
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
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
Code and manuscript for the paper "INFERNO: Inference-Aware Neural Optimisation". Automated mirror from CERN GitLab.
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Roundtrip: density estimation with deep generative neural networks
Likelihood-free AMortized Posterior Estimation with PyTorch
distributed, likelihood-free inference
Simulation-based inference toolkit
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