6 papers
Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
Julius Vetter, Manuel Gloeckler, Daniel Gedon +1
Simulation-based inference (SBI) offers a flexible and general approach to performing Bayesian inference: In SBI, a neural network is trained on synthetic data simulated from a mod…
Simulation-Based Inference: A Practical Guide
Michael Deistler, Jan Boelts, Peter Steinbach +11
A central challenge in many areas of science and engineering is to identify model parameters that are consistent with prior knowledge and empirical data. Bayesian inference offers…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Compositional simulation-based inference for time series
Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu +1
Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likeliho…
Inferring stochastic low-rank recurrent neural networks from neural data
Matthijs Pals, A Erdem SaÄtekin, Felix Pei +2
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should idea…
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
Sebastian Bischoff, Alana Darcher, Michael Deistler +18
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, prote…