6 citations · 12 across the 5 of their papers we have counts for
5 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…
Consistency Models for Scalable and Fast Simulation-Based Inference
Marvin Schmitt, Valentin Pratz, Ullrich Köthe +2
Simulation-based inference (SBI) is constantly in search of more expressive and efficient algorithms to accurately infer the parameters of complex simulation models. In line with t…
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…
JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models
Stefan T. Radev, Marvin Schmitt, Valentin Pratz +3
This work proposes ``jointly amortized neural approximation'' (JANA) of intractable likelihood functions and posterior densities arising in Bayesian surrogate modeling and simulati…