3 papers
stat.ML2026
Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
Philipp Reiser, Paul-Christian Bürkner, Anneli Guthke
Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inverse problems, sensitivity analysis, and…
stat.ML2026
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Stefania Scheurer, Philipp Reiser, Tim Brünnette +3
Bayesian inference typically relies on a large number of model evaluations to estimate posterior distributions. Established methods like Markov Chain Monte Carlo (MCMC) and Amortiz…
stat.ML2025
Uncertainty Quantification and Propagation in Surrogate-based Bayesian Inference
Philipp Reiser, Javier Enrique Aguilar, Anneli Guthke +1
Surrogate models are statistical or conceptual approximations for more complex simulation models. In this context, it is crucial to propagate the uncertainty induced by limited sim…