Abstract

In science and engineering, we often use computer simulations as models of real-world phenomena. Such simulation-based models allow us to build and test hypotheses about the processes underlying a phenomenon, e.g., by simulating synthetic data from a model and comparing it to observed data. A key challenge with this approach is finding model configurations that reproduce the observed data.

Bayesian statistical inference provides a principled way to address this challenge: It allows us to systematically infer suitable model parameters and quantify uncertainty. However, classical Bayesian inference methods typically require access to the likelihood function of the model and thus cannot be applied to the often highly complex scientific simulators.

With the increase in available computational resources and the advent of neural network-based machine learning methods, an alternative approach has recently emerged: simulation-based inference (SBI). SBI enables Bayesian parameter inference but only requires access to simulations from the model.

In this talk, I give a gentle introduction to SBI, present my research on new SBI methods for computational neuroscience, and comment on new software tools bridging the gap between methods and applicability of SBI.