Jul 2 – 5, 2023
Europe/Amsterdam timezone

Extremely Flexible Estimation of Unnormalized Posteriors with Simulation-based Inference

Not scheduled
Turingzaal (CWI)



Centrum voor Wiskunde en Informatica - Science Park 123, 1098 XG Amsterdam


Benjamin Miller (University of Amsterdam)


Suppose you have some observational data which is well modeled by a complex simulator. The inverse problem is: "Which parameters could be input into the simulator to reproduce the observational data?" Simulation-based Inference estimates the probabilistic solution to the inverse problem by creating a surrogate model for the posterior distribution.

We created a generalized method to estimate the posterior distribution given flexibly drawn training (i.e. simulation) data. We take advantage of extremely expressive estimators that take the "best-of-both-worlds" compared to other simulation-based inference methods. Our method is extremely data efficient by using a favorable estimator design in the "bias-variance tradeoff" sense. We present the method itself and results on various benchmarks.

Primary authors

Benjamin Miller (University of Amsterdam) Christoph Weniger (University of Amsterdam) Dr Patrick Forré (University of Amsterdam)

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