Survival Modeling with Turing.jl

Follow-up

We will link to the recording here as soon as it is uploaded.

Type: SeminarSpeaker: Xianda SunDate & time: Tue May 13 2025, 15:00-16:30 (CEST)Place: Zoom

This is an online event. Zoom link will be provided here and through the email list.

In this seminar, Xianda will introduce Turing.jl and the Julia probabilistic programming ecosystem. He will demonstrate how the application of these tools with a sequence of survival models, progressing from a simple Weibull model to more complex models.

Turing.jl stands out for its flexibility, composability, and performance, offering a seamless modeling experience powered by Julia’s just-in-time (JIT) compilation. It allows researchers to write models in an intuitive syntax close to mathematical notation while benefiting from high-performance inference engines under the hood.

Xianda Sun is a PhD student working on Turing.jl and JuliaBUGS.jl in Hong Ge’s lab.

For epidemiologists and biostatisticians, adopting Turing.jl offers several key advantages:

Speed and scalability: Julia’s performance rivals that of C, making Turing.jl well-suited for large-scale simulation and inference tasks.
Expressiveness: Define complex, custom models — including those with non-standard likelihoods or intricate dependencies — without sacrificing clarity or flexibility.
Interoperability: Leverage the rich Julia ecosystem, including data manipulation, plotting, and differential equation solvers.
Reproducibility and openness: As a fully open-source toolchain, Turing.jl encourages transparent, shareable, and extensible science.

For an in-depth look at Turing.jl, its design and applications, check out the recent publication by Fjelde et al. (2025).

Coming from R or Python? Julia has great interfaces to these languages with RCall.jl and PythonCall.jl, allowing you to seemlessly integrate your existing workflows with Julia!