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Found 5 papers, 4 papers with code
GATSBI: Generative Adversarial Training for Simulation-Based Inference
We study the relationship between SBI and GANs, and introduce GATSBI, an adversarial approach to SBI.
Benchmarking Simulation-Based Inference
We set out to fill this gap: We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms including recent approaches employing neural networks and classical Approximate Bayesian Computation methods.
SBI -- A toolkit for simulation-based inference
$\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
Likelihood-free inference with emulator networks
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods.
Flexible statistical inference for mechanistic models of neural dynamics
Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters.
