Simulation-based inference toolkit
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Updated
Jul 19, 2024 - Python
Simulation-based inference toolkit
Likelihood-free AMortized Posterior Estimation with PyTorch
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
A Python toolkit for (simulation-based) inference and the mechanization of science.
(NeurIPS 2022) Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination
Simulation-based inference in JAX
Conduct simulation-based inference on strong gravitational lensing systems.
A simulation-based Inference (SBI) library designed to perform analysis on a wide class of gravitational wave signals
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
Simulation-based inference using SSNL
Arbitrary Marginal Neural Ratio Estimation for Likelihood-free Inference
Created for benchmarking different techniques to search for CP violation in the HWW vertex in leptonic WH production.
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.
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