Sharan Yalburgi from Juliahub gives us a primer on the various ways to use probabilistic programming, its implementation in Julia, and the potential for incorporating Gaussian Processes in Julia-based probabilistic programming workflows.

This technical session will motivate and introduce probabilistic programming and explore its benefits and applications. We will examine the advantages of probabilistic models in terms of uncertainty quantification, model interpretability, and efficient use of limited data.

Specifically, we will delve into the usage of probabilistic programming in Julia, with a focus on Turing.jl and its automated and customisable inference. Additionally, we will explore the Gaussian Processes ecosystem in Julia and its potential for incorporation into Turing.jl.

The YouTube link for the event.