ExStan: Elixir 🤝 Stan for Probabilistic Modeling

Shubham Gupta


Dive into the fundamentals of probabilistic programming and Bayesian inference in this introductory session. The session will emphasize how Stan, a renowned tool for sophisticated statistical modeling, is seamlessly integrated with Elixir via the ExStan package. This integration is not just theoretical; participants will witness its practical application through a focused case study on predicting the 2022 Football World Cup outcomes. This real-world example will illuminate ExStan’s capabilities in model fitting, rigorous testing, and thorough evaluation. Attendees will depart with a comprehensive understanding of ExStan’s current functionalities, a preview of its promising future developments, and a curated list of resources for further exploration. This session is an opportunity to grasp the essentials of probabilistic programming and understand how it can be applied to solve complex problems in various domains using ExStan.

Key takeaways:

  • A foundational grasp of probabilistic programming and Bayesian inference, tailored for those new to the field.
  • An in-depth understanding of how ExStan seamlessly integrates the power of Stan with the flexibility of Elixir.
  • Practical knowledge from a real-world case study on the 2022 Football World Cup predictions using ExStan.
  • Comprehensive insights into model development, from fitting and testing to evaluation, within the ExStan framework.
  • A foresight into the evolving landscape of ExStan, including its potential future applications and enhancements.


  • Software developers and programmers who are keen to explore the intersection of Elixir and advanced statistical modeling.
  • Data scientists and analysts interested in leveraging the power of Stan for probabilistic modeling within the Elixir ecosystem.
  • Professionals in industries reliant on data-driven decision-making, looking to understand how probabilistic programming can be applied in real-world scenarios.

Level: Introductory and overview

Tags: Probabilistic Programming, Statistical Modelling, ExStan

Format: Virtual