In the world of the research fields drifting further apart from one another and Artificial Intelligence (AI) tools gaining increasingly more attention, methods which can bring a number of seemingly disjoint fields together is of the utmost importance. The proposed tutorial is sought to provide researchers with an instrument to unify Bayesian modelling, large-scale simulations, and AI methods while integrating them into the HPC environment.
While Bayesian inference is widely used in the research community, as it provides distributional estimates of model parameters and allows to update the model by incorporating new data into it, it often suffers from computationally intensive processes and limited parallelization capabilities. Simulation Based Inference (SBI) is a tool to tackle this issue.
SBI employs AI-based approximate Bayesian computation to dramatically reduce inference times and generate reliable estimates, even when observed data are sparse. This approach enables any representative simulation model to inform parameter constraints, yielding approximate posterior distributions. Additionally, SBI facilitates workload distribution across high-performance computing clusters, further reducing runtime.
This tutorial discusses theoretical foundations and provides practical training for constructing SBI frameworks tailored to specific models. Through provided examples, participants will gain insight into various levels of model granularity, ranging from a simple black box approach to a highly customizable design, and develop the skills to effectively manage HPC devices within a given set-up. By participating in this tutorial, attendees will gain an understanding of the principles of Simulation Based Inference, learn how to apply this methodology in the context of HPC in a variety of case scenarios, to evaluate its potential and utility, and be encouraged to consider its applicability to their own research projects.
Tutorial materials are available here, and a full description is on GitHub.
