# ONNX Probabilistic Programming Working Group – Collaboration with the Julia PPL Ecosystem (Turing.jl / RxInfer.jl)

**URL:** <https://discourse.julialang.org/t/onnx-probabilistic-programming-working-group-collaboration-with-the-julia-ppl-ecosystem-turing-jl-rxinfer-jl/136087>\
**Category:** Community\
**Tags:** probablistic\
**Created:** [March 7, 2026, 8:10pm UTC](https://discourse.julialang.org/t/onnx-probabilistic-programming-working-group-collaboration-with-the-julia-ppl-ecosystem-turing-jl-rxinfer-jl/136087 "2026-03-07T20:10:10Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![bparbhu](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/bparbhu/32/4545_2.png) [@bparbhu](https://discourse.julialang.org/u/bparbhu)\
**Post date:** [March 7, 2026, 8:10pm UTC](https://discourse.julialang.org/t/onnx-probabilistic-programming-working-group-collaboration-with-the-julia-ppl-ecosystem-turing-jl-rxinfer-jl/136087/1 "2026-03-07T20:10:10Z")

</div>

Hi everyone,

I wanted to share that the **ONNX Probabilistic Programming Working Group** has recently been formed and invite participation from the Julia probabilistic programming community—particularly developers and users of **Turing.jl, RxInfer.jl, DynamicPPL, Bijectors.jl, and related packages**.

The goal of this working group is to bring **probabilistic modeling and Bayesian inference into the ONNX ecosystem as first-class capabilities** , similar to how ONNX already supports portable neural network models.

We are working toward defining a standardized operator domain and runtime semantics that allow probabilistic models to be **exported, executed, and optimized across frameworks and hardware**.

### Areas we are exploring

Some of the areas the working group is currently focusing on include:

- Probability distributions and log-density operators
- Bijectors and constrained parameter transformations
- Reproducible **stateless, splittable RNG semantics**
- Special mathematical functions required for probabilistic inference
- Inference algorithms such as **Laplace, Pathfinder, INLA, HMC, NUTS, and SMC**
- Export pathways for probabilistic programming frameworks

### Frameworks we are looking to support

The working group is interested in supporting a range of probabilistic programming systems, including:

- Stan
- PyMC
- Pyro
- NumPyro
- TensorFlow Probability
- JAX-based probabilistic systems
- BayesFlow
- **Julia probabilistic programming frameworks including Turing.jl and RxInfer.jl**

The goal is to make probabilistic models **portable across frameworks and hardware backends using ONNX as an intermediate representation** , while preserving the semantics required for probabilistic inference.

### Why input from the Julia community matters

The Julia ecosystem has developed some of the most interesting probabilistic programming infrastructure in recent years. In particular:

- **Turing.jl / DynamicPPL** for flexible Bayesian modeling and inference
- **RxInfer.jl** for reactive message-passing and variational inference
- **Bijectors.jl** and **Distributions.jl** for composable probabilistic building blocks

We would really value perspectives from the Julia community on how these abstractions should be represented in a portable IR.

### Getting involved

If you’re interested in participating, contributing ideas, or providing feedback from the Julia ecosystem perspective, feel free to reach out to:

- **Andreas Fehlner** [https://www.linkedin.com/in/andreas-fehlner-60499971/](https://www.linkedin.com/in/andreas-fehlner-60499971/)
- **Adam Pocock** [https://www.linkedin.com/in/craigacp/](https://www.linkedin.com/in/craigacp/)
- **Brian Parbhu** [https://www.linkedin.com/in/brian-parbhu-99891133/](https://www.linkedin.com/in/brian-parbhu-99891133/)

You are also welcome to attend the working group meetings:

🗓 **Fridays @ 12 PM EST, every two weeks**

Working group repository:

> **[working-groups/probabilistic-programming at main · onnx/working-groups](https://github.com/onnx/working-groups/tree/main/probabilistic-programming)**
>
> Repository for ONNX working group artifacts. Contribute to onnx/working-groups development by creating an account on GitHub.