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stat.ML2025
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.ML2024
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.ML2023
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…