7 citations · 24 across the 14 of their papers we have counts for
7 papers · 1 filter
Amortized Bayesian Workflow
Chengkun Li, Aki Vehtari, Paul-Christian Bürkner +3
Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-st…
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks: An Extended Investigation
Marvin Schmitt, Paul-Christian Bürkner, Ullrich Köthe +1
Recent advances in probabilistic deep learning enable efficient amortized Bayesian inference in settings where the likelihood function is only implicitly defined by a simulation pr…
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…
Fuse It or Lose It: Deep Fusion for Multimodal Simulation-Based Inference
Marvin Schmitt, Leona Odole, Stefan T. Radev +1
We present multimodal neural posterior estimation (MultiNPE), a method to integrate heterogeneous data from different sources in simulation-based inference with neural networks. In…
Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
Marvin Schmitt, Desi R. Ivanova, Daniel Habermann +3
We propose a method to improve the efficiency and accuracy of amortized Bayesian inference by leveraging universal symmetries in the joint probabilistic model of parameters and dat…
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…