52 citations · 167 across the 19 of their papers we have counts for
6 papers · 1 filter
JADAI: Jointly Amortizing Adaptive Design and Bayesian Inference
Niels Bracher, Lars Kühmichel, Desi R. Ivanova +3
We consider problems of parameter estimation where design variables can be actively optimized to maximize information gain. To this end, we introduce JADAI, a framework that jointl…
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…
Amortized Bayesian Mixture Models
Šimon Kucharský, Paul Christian Bürkner
Finite mixtures are a broad class of models useful in scenarios where observed data is generated by multiple distinct processes but without explicit information about the responsib…
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…
Sensitivity-Aware Amortized Bayesian Inference
Lasse Elsemüller, Hans Olischläger, Marvin Schmitt +3
Sensitivity analyses reveal the influence of various modeling choices on the outcomes of statistical analyses. While theoretically appealing, they are overwhelmingly inefficient fo…
Amortized Bayesian model comparison with evidential deep learning
Stefan T. Radev, Marco D'Alessandro, Ulf K. Mertens +3
Comparing competing mathematical models of complex natural processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way t…