# Gaussian Process Model with Turing

**URL:** https://discourse.julialang.org/t/gaussian-process-model-with-turing/42453
**Category:** Probabilistic Programming
**Tags:** regression, turing, gaussian-process
**Created:** [July 2, 2020, 9:10pm UTC](https://discourse.julialang.org/t/gaussian-process-model-with-turing/42453 "2020-07-02T21:10:36Z")
**Posts on this page:** 1
**Showing post:** 52

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### Author: ![ElOceanografo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/eloceanografo/32/624_2.png) [@ElOceanografo](https://discourse.julialang.org/u/ElOceanografo)
#### Post date: [October 8, 2020, 11:30pm UTC](https://discourse.julialang.org/t/gaussian-process-model-with-turing/42453/52 "2020-10-08T23:30:31Z")

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FYI, theres a [PR](https://github.com/willtebbutt/Stheno.jl/pull/136) to Stheno right now that will let you use Sparse GPs in Turing. Not battle-tested yet, but I’ve got it to run for some simple problems. And here’s the thread that led to it, if you didn’t see it:

> [@Implementing sparse Gaussian process model with Turing and Stheno](https://discourse.julialang.org/t/implementing-sparse-gaussian-process-model-with-turing-and-stheno/46886/7):
>
> Okay, I came back to this and after some hacking have got the following to run and produce basically sensible results. struct SparseFiniteGP{T1\<:Stheno.FiniteGP, T2\<:Stheno.FiniteGP} \<: Distributions.AbstractMvNormal fobs::T1 finducing::T2 end function Stheno.logpdf(f::T, y::AbstractArray{T1, 1}) where {T \<: SparseFiniteGP, T1 \<: Real} return elbo(f.fobs, y, f.finducing) end Base.length(f::SparseFiniteGP) = length(f.fobs) Distributions.\_rand!(rng::Random.\_GLOBAL\_RNG, f::SparseFini…

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