A Python implementation of EP-ABC for likelihood-free, probabilistic inference.
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Updated
Jun 22, 2018 - Python
A Python implementation of EP-ABC for likelihood-free, probabilistic inference.
Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.
Density estimation likelihood-free inference. No longer actively developed see https://github.com/mackelab/sbi instead
Likelihood-Free Inference for Julia.
A Python toolkit for (simulation-based) inference and the mechanization of science.
Inverse binomial sampling for efficient log-likelihood estimation of simulator models (old location)
Mining gold from implicit models to improve likelihood-free inference, example for ROLR and RASCAL.
Approximate Bayesian Computation (ABC) with differential evolution (de) moves and model evidence (Z) estimates.
Inverse binomial sampling for efficient log-likelihood estimation of simulator models in Python
Inverse binomial sampling for efficient log-likelihood estimation of simulator models in MATLAB
ELFI - Engine for Likelihood-Free Inference
Julia package for neural estimation
A Python library for amortized Bayesian workflows using generative neural networks.
R package for statistical inference using partially observed Markov processes
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