Interactive Gradio app for visualizing experiments from our paper on learning summary statistics for Bayesian inference with autoencoders.
This project provides an interactive dataset explorer for the paper Learning Summary Statistics for Bayesian Inference with Autoencoders.
The app is intended as a lightweight companion to the paper, making it easier to inspect the datasets used in the experiments and understand the structure of the simulation outputs before applying learned summary statistics for Bayesian inference.
For approximate Bayesian computation with intractable likelihoods, proposes using latent representations from autoencoders as learned summary statistics. The decoder receives explicit/implicit noise so the encoder isolates parameter-relevant signal; validated on two stochastic model classes.
@article{albert2022learning,title={Learning summary statistics for bayesian inference with autoencoders},author={Albert, Carlo and Ulzega, Simone and Ozdemir, Firat and Perez-Cruz, Fernando and Mira, Antonietta},journal={SciPost Physics Core},volume={5},number={3},pages={043},year={2022},doi={10.21468/SciPostPhysCore.5.3.043},preview_alt={ENCA and INCA architectures above latent summary-statistic distributions.},preview_note={Thumbnail adapted from the corresponding paper.}}
