Hi All, I was going through Bill Engels zero-inflated Poisson Likelihood GP example here: https://bwengals.github.io/gps-with-non-normal-likelihoods-in-pymc3.html I see the following code: with pm.Model() as model: ℓ = pm.Gamma("ℓ", alpha=2, beta=2) cov = pm.gp.cov.ExpQuad(1, ℓ) c =…

I think you should use gp.Latent for that, see https://docs.pymc.io/notebooks/GP-Latent.html for more information. Specifically, you replace f = pm.gp.GP("f", X=x[:,None], mean_func=mu, cov_func=cov) with: gp = pm.gp.Latent(mean_func=mu, cov_func=cov) f = gp.prior("f", X=x[:,None]) cc @bw…

Zero-Inflated Poisson Example

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junpenglao September 24, 2019, 7:54pm 4

Yes, custom log likelihood could be implemented using pm.DensityDist

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Zero-Inflated Poisson Example

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