Community-sourced list of papers and resources on neural simulation-based inference.

Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods

Based on BayesFlow, we develop a stochastic BayesFlow algorithm to solve stochastic inverse problems and validate it using the inverse uncertainty quantification of a simulated vehicle dynamics model.

Contains the code for reproducing the experiments and results of the paper "Neural Superstatistics: A Bayesian Method for Estimating Dynamic Models of Cognition".

Code accompanying the paper "A Deep Learning Method for Comparing Bayesian Hierarchical Models".

Contains the code accompanying the paper "JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models"

Likelihood-Free Frequentist Inference

Normalizing-flow enhanced sampling package for probabilistic inference in Jax

A High-Level Plotting Interface for Blender

Neural drift-diffusion model (NDDM) is a repository to integrate simultaneously both single-trial EEG measures and behavioral performance (response time and accuracy) to understand cognition.

A Python library for amortized Bayesian workflows using generative neural networks.