I’ve built a Turing model for some non-Gaussian data which contains a latent Gaussian process for spatial autocorrelation. I’ve run it on a small simulated dataset to confirm that it works. Now I want to run it on my real data–which includes about 11,000 data points, so impractical for a dense GP.
A simple MWE is below (the real model more complicated, but the essence is the same, i.e. GP => transformation => parameter of data distribution). Is there a simple(ish) way to modify this to use the sparse inducing-point approximations available in Stheno instead of the full GP?
using Turing, Stheno, Distributions, Random Random.seed!(1) x = 1.0:10.0 y = rand(GP(Matern32(), GPC())(x)) λ = exp.(y) z = rand.(Poisson.(λ)) @model function example_model(x, z) y ~ GP(Matern32(), GPC())(x) λ = exp.(y) z .~ Poisson.(λ) end mod = example_model(x, z) chn = sample(mod, NUTS(), 100)