4 citations · 10 across the 4 of their papers we have counts for
4 papers
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Lars Kühmichel, Jerry M. Huang, Valentin Pratz +11
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows.…
Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation
Lasse Elsemüller, Valentin Pratz, Mischa von Krause +3
Neural networks are fragile when confronted with data that significantly deviates from their training distribution. This is true in particular for simulation-based inference method…
BayesFlow: Amortized Bayesian Workflows With Neural Networks
Stefan T Radev, Marvin Schmitt, Lukas Schumacher +5
Modern Bayesian inference involves a mixture of computational techniques for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflo…
A Deep Learning Method for Comparing Bayesian Hierarchical Models
Lasse Elsemüller, Martin Schnuerch, Paul-Christian Bürkner +1
Bayesian model comparison (BMC) offers a principled approach for assessing the relative merits of competing computational models and propagating uncertainty into model selection de…