Open albazarova opened 5 months ago
Hi @albazarova do you have a schedule or any other information I should put on the website?
This is an approximate schedule. Breaks are to be discussed, of course:)
Course schedule:
14.00 – 14.20 Introduction, tutorial overview, onboarding to HPC system
Teaching content
Learning Goals
14.20 – 14.40 Lecture: Basic concepts of classical Bayesian inference
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14.40 – 14.55 Hands-on: warm-up example in a Jupyter notebook
Teaching content
A simple coin-flipping example implemented within Jupyter notebook
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14.55 - 15.10 Lecture: basic concepts of Simulation Based Inference
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15.10-15.30 Hands-on: Converting classical Bayesian example into an SBI one, Jupyter notebook
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15.30 - 16.00 Coffee-break
16.00 - 16.30 Hands-on: data example. MCMC vs SBI, Jupyter notebook
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16.30 - 16.45 Lecture: Deep Learning component and Sequential estimation
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16.45 – 17.15 Hands-on: flexible interface of the sbi package, Jupyter notebook
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17.15 – 17.35 Hands-on: Constructing a summary statistic
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17.35 – 18.00 Parallelization and distributing SBI over multiple nodes
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Scale up the simulations in order to reduce the running time
@albazarova thank you! Will add this to the website, too. According to our overall schedule the afternoon events start at 13:00 or 13:15? Shall I adapt this timetable accordingly, i.e., start at 13:15 and add a 2nd break somewhere?
@SusanneWenzel sure, no objections:)
Title
Introduction to Simulation Based Inference: enhancing synthetic models with Artificial Intelligence
Responsible person(s)
Alina Bazarova, Stefan Kesselheim, Forschungszentrum Jülich, Jülich Supercomputing Center
Format
Tutorial
Timeframe
4 hours
Description
Artificial intelligence (AI) techniques are constantly changing scientific research, but their potential to enhance simulation pipelines is not widely recognised. Conversely, Bayesian inference is a well-established method in the research community, offering distributional estimates of model parameters and the ability to update models with new data. However, traditional Bayesian inference often faces computational challenges and limited parallelisation capabilities.
Simulation Based Inference (SBI) presents a comprehensive solution by combining simulations, AI techniques, and Bayesian methods. SBI utilizes AI-driven approximate Bayesian computation to significantly reduce inference times and produce reliable estimates, even with sparse observed data. This approach allows any representative simulation model to inform parameter constraints, leading to approximate posterior distributions. Furthermore, SBI enables workload distribution across high-performance computing clusters, further decreasing runtime.
This tutorial explores the theoretical foundations and provides hands-on training for constructing tailored SBI frameworks for specific models. Through practical examples, participants will gain insights into different levels of model granularity, ranging from a simple black box approach to a highly customizable design. By participating in this tutorial, attendees will develop the skills necessary to implement Simulation Based Inference in their own research projects.
Topics to be covered:
Requirements