96 citations · 280 across the 17 of their papers we have counts for
7 papers · 1 filter
Amortized Bayesian Decision Making for simulation-based models
Mila Gorecki, Jakob H. Macke, Michael Deistler
Simulation-based inference (SBI) provides a powerful framework for inferring posterior distributions of stochastic simulators in a wide range of domains. In many settings, however,…
Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers
Guy Moss, Vjeran Višnjević, Olaf Eisen +4
The ice shelves buttressing the Antarctic ice sheet determine the rate of ice-discharge into the surrounding oceans. The geometry of ice shelves, and hence their buttressing streng…
Adversarial robustness of amortized Bayesian inference
Manuel Glöckler, Michael Deistler, Jakob H. Macke
Bayesian inference usually requires running potentially costly inference procedures separately for every new observation. In contrast, the idea of amortized Bayesian inference is t…
Flow Matching for Scalable Simulation-Based Inference
Maximilian Dax, Jonas Wildberger, Simon Buchholz +3
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional pro…
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Richard Gao, Michael Deistler, Jakob H. Macke
Simulation-based inference (SBI) enables amortized Bayesian inference for simulators with implicit likelihoods. But when we are primarily interested in the quality of predictive si…
Simultaneous identification of models and parameters of scientific simulators
Cornelius Schröder, Jakob H. Macke
Many scientific models are composed of multiple discrete components, and scientists often make heuristic decisions about which components to include. Bayesian inference provides a…