2 citations · 5 across the 6 of their papers we have counts for
8 papers
Conditional Mean Flows: Few-Step Densities for Simulation-Based Inference
Jonas Arruda, Jan Hasenauer, Stefan T. Radev
Flow matching has emerged as a powerful generative family for simulation-based inference (SBI). However, sampling and density evaluation require numerical integration at inference…
Hierarchical Bayesian inference with compositional score modeling for stellar streams
Giuseppe Viterbo, Jonas Arruda, Tobias Buck
Context: Stellar streams trace the gravitational potential of the Milky Way over a wide range of Galactocentric radii. Since different streams sample different regions of the Galax…
NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia
Manuel Huth, Jonas Arruda, Nina Schmid +4
Nonlinear mixed-effects models are widely used to analyze longitudinal data, but existing open-source software often supports only a limited subset of the model structures, inferen…
Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Jonas Arruda, Sophie Chervet, Paula Staudt +6
Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in est…
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Lars Kühmichel, Jerry M. Huang, Valentin Pratz +11
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows.…
MEmilio -- A high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics
Julia Bicker, Carlotta Gerstein, David Kerkmann +33
Epidemic and pandemic preparedness with rapid outbreak response rely on timely, trustworthy evidence. Mathematical models are crucial for supporting timely and reliable evidence ge…
Diffusion Models in Simulation-Based Inference: A Tutorial Review
Jonas Arruda, Niels Bracher, Ullrich Köthe +2
Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real…
Compositional amortized inference for large-scale hierarchical Bayesian models
Jonas Arruda, Vikas Pandey, Catherine Sherry +4
Amortized Bayesian inference (ABI) with neural networks has emerged as a powerful simulation-based approach for estimating complex mechanistic models. However, extending ABI to hie…