1 code implementation • 15 Apr 2024 • Manuel Gloeckler, Michael Deistler, Christian Weilbach, Frank Wood, Jakob H. Macke
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data.
1 code implementation • 19 Mar 2024 • Sebastian Bischoff, Alana Darcher, Michael Deistler, Richard Gao, Franziska Gerken, Manuel Gloeckler, Lisa Haxel, Jaivardhan Kapoor, Janne K Lappalainen, Jakob H Macke, Guy Moss, Matthijs Pals, Felix Pei, Rachel Rapp, A Erdem Sağtekin, Cornelius Schröder, Auguste Schulz, Zinovia Stefanidi, Shoji Toyota, Linda Ulmer, Julius Vetter
To demonstrate how these distances are used in practice, we evaluate generative models from different scientific domains, namely a model of decision making and a model generating medical images.
1 code implementation • 19 Feb 2024 • Jonas Beck, Nathanael Bosch, Michael Deistler, Kyra L. Kadhim, Jakob H. Macke, Philipp Hennig, Philipp Berens
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging.
1 code implementation • 5 Dec 2023 • 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.
1 code implementation • 24 May 2023 • Manuel Glöckler, Michael Deistler, Jakob H. Macke
Bayesian inference usually requires running potentially costly inference procedures separately for every new observation.
no code implementations • NeurIPS 2023 • Richard Gao, Michael Deistler, Jakob H. Macke
Generalized Bayesian Inference (GBI) aims to robustify inference for (misspecified) simulator models, replacing the likelihood-function with a cost function that evaluates the goodness of parameters relative to data.
1 code implementation • 21 Oct 2022 • Jonas Beck, Michael Deistler, Yves Bernaerts, Jakob Macke, Philipp Berens
To this end, many SBI methods employ a set of summary statistics or scientifically interpretable features to estimate a surrogate likelihood or posterior.
1 code implementation • 10 Oct 2022 • Michael Deistler, Pedro J Goncalves, Jakob H Macke
TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches.
1 code implementation • ICLR 2022 • Manuel Glöckler, Michael Deistler, Jakob H. Macke
We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods.
1 code implementation • ICLR 2022 • Maximilian Dax, Stephen R. Green, Jonathan Gair, Michael Deistler, Bernhard Schölkopf, Jakob H. Macke
We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data.
no code implementations • 17 Jul 2020 • Alvaro Tejero-Cantero, Jan Boelts, Michael Deistler, Jan-Matthis Lueckmann, Conor Durkan, Pedro J. Gonçalves, David S. Greenberg, Jakob H. Macke
$\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
no code implementations • 25 Jun 2019 • Michael Deistler, Yagmur Yener, Florian Bergner, Pablo Lanillos, Gordon Cheng
In this work, we investigate the generation of tactile hallucinations on biologically inspired, artificial skin.