Simulation-based inference toolkit
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Updated
Jun 21, 2024 - Python
Simulation-based inference toolkit
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
Conduct simulation-based inference on strong gravitational lensing systems.
Likelihood-free AMortized Posterior Estimation with PyTorch
A Python toolkit for (simulation-based) inference and the mechanization of science.
A simulation-based Inference (SBI) library designed to perform analysis on a wide class of gravitational wave signals
Simulation-based inference in JAX
Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference
(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Created for benchmarking different techniques to search for CP violation in the HWW vertex in leptonic WH production.
Simulation-based inference using SSNL
pyLFI is a Python toolbox using likelihood-free inference (LFI) methods for estimating the posterior distributions of model parameters.
Comparison of summary statistic selection methods with a unifying perspective.
Code for reproducing the experiments in the paper Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference.
Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
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