November 9, 2023, 15:00 – 18:00 hrs
An Introduction to the Julia Programming Language | A fresh approach to scientific computing
Hannes Diemerling, Maximilian Ernst, Moritz Ketzer, Aaron Peikert
Julia is a high-level dynamic programming language designed for numerical and scientific computing, data analysis, machine learning, and more. Julia's user-friendly interface, resembling popular languages like R, Python, and Matlab, makes it easily accessible for learners. Ist focus on performance, however, enables computational speeds that often rival, or even surpass, those of low-level languages such as C or Fortran. Therefore, Julia is an ideal choice for large-scale data analysis and other computationally intensive tasks, as it can be orders of magnitude faster than R or Python. In this workshop, we will introduce you to the basics of Julia and the key features that set it apart from other languages.
This workshop is designed for R/Python/Matlab users interested in learning a new programming language or wanting to improve their data analysis and scientific computing skills. No prior experience with Julia is required, but understanding a programming language is recommended.
To get started head to the website of the workshop: https://formal-methods-mpi.github.io/Workshop.jl/stable/
Thinking Bayesian Statistics
Timo von Oertzen
Analyzing data with Bayesian statistics needs a lot of rethinking, but for most application no new software skills. Programs you always used can do most of what Bayesian analyses need. In this workshop, we will discuss what Bayesian methods really mean, why we still need to think (even more) although we use Bayesian statistics (in particular, why sub-dimensional tests are still impossible), how we can use old tools for most of analyses, and a little glance at the new tools if we want to do real fancy Bayesian.