# #154 Bayesian Causal Inference at Scale, with Thomas Pinder

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Podcast: [Learning Bayesian Statistics](https://stenobird.com/podcast/learning-bayesian-statistics)
Published: 2026-03-25T12:31:33+00:00
Episode link: https://api.riverside.com/hosting-analytics/media/9432fde2730231f273503a54c6596478098385dc262a12de0ac994c8667b1e74/eyJlcGlzb2RlSWQiOiI0OGRlYTdkMi0xOTEzLTQ0YWUtODdlNi01NzMwOGEzY2EwMWIiLCJwb2RjYXN0SWQiOiI3OWUwYTRmYi05N2FiLTRlOTUtYTg3NS0yNGE4YjllZTI3ZGEiLCJhY2NvdW50SWQiOiI2NDQ4M2JiZWM3ZjQ1MTFhYThjMzE1Y2QiLCJwYXRoIjoibWVkaWEvY2xpcHMvNjliZWJjNmYwMDFkYzgzZmNmM2NkZjIxL2FsZXhhbmRyZS1hbmRvcnJhcy1zdHVkaW8tY29tcG9zZXItMjAyNi0zLTIxX18xNi00Mi0zOS5tcDMifQ==.mp3
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Duration seconds: 5178

## Resource

• Support & get perks ! • Bayesian Modeling course (first 2 lessons free) Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work ! Takeaways : Q: Why was GPJax created and how does it benefit researchers? A: GPJax was developed to provide a high-performance, flexible framework for Gaussian processes (GPs) within the JAX ecosystem. It allows researchers to move beyond black-box implementations and easily experiment with custom kernels and model structures while leveraging JAX’s automatic differentiation and GPU acceleration. Q: What are the primary advantages of using Gaussian processes for data modeling? A: Gaussian processes are highly effective at modeling complex, nonlinear relationships in data. Unlike many machine learning methods that only provide a point estimate, GPs offer built-in uncertainty quantification, which is essential for understanding the reliability of predictions in research and industry. Q: How does the GPJax and NumPyro integration enhance probabilistic modeling? A: The integration allows users to treat GPJax models as components within a larger NumPyro probabilistic program. This combination enables the use of advanced sampling techniques like NUTS (No-U-Turn Sampler), making it easier to build and fit complex hierarchical models that include Gaussian processes. Q: What are the main challenges when applying Gaussian processes to high-dimensional data? A: High-dimensional data significantly complicates GP modeling due to the curse of dimensionality and the cubic scaling of computational costs. In high dimensions, defining meaningful distance metrics for kernels becomes harder, often requiring specialized techniques like sparse GPs or dimensionality reduction to remain tractable. Full tak…

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