Probabilistic programming for statistical analysis in Julia
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This tutorial is part of the COST action HiTEc, prepared for the CFE-CMStatistics 2025 conference in London.
Julia has emerged as an important language for statistical data analysis and machine learning. It is a high-level language that is easy to learn, but with a speed close to C/C++ from its just-in-time compilation. Despite its relatively young age, Julia already has an impressive set of libraries for statistics, and can be easily integrated with a workflow in R or Python.
This first half of this tutorial introduces the Julia programming language with a focus on statistical analysis. The second half focuses on likelihood and Bayesian inference using the Turing.jl probabilistic programming ecosystem in Julia. Participants are encouraged to install Julia and some statistical packages before the tutorial, to follow along on their own laptops.
Venue
The tutorial takes place in MAL 404 + 405, Floor 4, Birkbeck Malet Street (main building, Stairs A, Lift A1, A2), Birkbeck, University of London, UK. For virtual participation, please see below.
The registration and coffee breaks with take place in MAL 151, Birkbeck Malet Street (main building, Stairs B, Lifts B1, B2).
Part 1 - Introduction to the Julia programming language
Instructor: Mattias Villani, Professor of Statistics, Stockholm University.
Time: 9.00-13.00 with a half-hour coffee break at 10.30-11.00.
Material for Part 1: available at the website Julia4Stats, including instructions on how to install Julia.
Part 2 - Probabilistic programming for statistical analysis in Turing.jl
Instructors:
Markus Hauru, Senior Research Data Scientist, Alan Turing Institute
Penelope Yong, Research Data Scientist, Alan Turing Institute
Tim Hargreaves, PhD candidate, University of Cambridge
Time: 14.30-18.30 with a half-hour coffee break at 16.00-16.30.
Material for Part 2:
Github repository for all four parts.
Google Colab notebooks: