5 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…
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…
Latent Diffusion for Neural Spiking Data
Jaivardhan Kapoor, Auguste Schulz, Julius Vetter +3
Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While laten…
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation
Julius Vetter, Guy Moss, Cornelius Schröder +2
Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations - an inference task also known as source distributi…
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…